| Title: | An interface to the BOOST graph library |
|---|---|
| Description: | A fairly extensive and comprehensive interface to the graph algorithms contained in the BOOST library. |
| Authors: | Vince Carey [aut], Li Long [aut], R. Gentleman [aut], Emmanuel Taiwo [ctb] (Converted RBGL vignette from Sweave to RMarkdown / HTML.), Bioconductor Package Maintainer [cre] |
| Maintainer: | Bioconductor Package Maintainer <[email protected]> |
| License: | Artistic-2.0 |
| Version: | 1.83.0 |
| Built: | 2024-12-03 06:14:27 UTC |
| Source: | https://github.com/bioc/RBGL |
Compute astarSearch for a graph
astarSearch(g)astarSearch(g)
g |
an instance of the |
NOT IMPLEMENTED YET. TO BE FILLED IN
a string indicating non-implementation
Li Long <[email protected]>
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
The Boost Graph Library: User Guide and Reference Manual; by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine; (Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0-201-72914-8
con <- file(system.file("XML/dijkex.gxl",package="RBGL"), open="r") coex <- fromGXL(con) close(con) astarSearch(coex)con <- file(system.file("XML/dijkex.gxl",package="RBGL"), open="r") coex <- fromGXL(con) close(con) astarSearch(coex)
Compute bandwidth for an undirected graph
bandwidth(g)bandwidth(g)
g |
an instance of the |
The bandwidth of an undirected graph G=(V, E) is the maximum distance between two adjacent vertices. See documentation on bandwidth in Boost Graph Library for more details.
bandwidth |
the bandwidth of the given graph |
Li Long <[email protected]>
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
The Boost Graph Library: User Guide and Reference Manual; by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine; (Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0-201-72914-8
con <- file(system.file("XML/dijkex.gxl",package="RBGL"), open="r") coex <- fromGXL(con) close(con) coex <- ugraph(coex) bandwidth(coex)con <- file(system.file("XML/dijkex.gxl",package="RBGL"), open="r") coex <- fromGXL(con) close(con) coex <- ugraph(coex) bandwidth(coex)
Algorithm for the single-source shortest paths problem for a graph with both positive and negative edge weights.
bellman.ford.sp(g,start=nodes(g)[1])bellman.ford.sp(g,start=nodes(g)[1])
g |
instance of class graph |
start |
character: node name for start of path |
This function interfaces to the Boost graph library C++ routines for Bellman-Ford shortest paths. Choose the appropriate algorithm to calculate the shortest path carefully based on the properties of the given graph. See documentation on Bellman-Ford algorithm in Boost Graph Library for more details.
A list with elements:
all edges minimized |
true if all edges are minimized, false otherwise. |
distance |
The vector of distances from |
penult |
A vector of indices
(in |
. For example, if the
element one of this vector has value 10, that means that the
predecessor of node 1 is node 10. The next predecessor is
found by examining penult[10].
start |
The start node that was supplied in the call to
|
Li Long <[email protected]>
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
The Boost Graph Library: User Guide and Reference Manual; by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine; (Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0-201-72914-8
dag.sp, dijkstra.sp, johnson.all.pairs.sp, sp.between
con <- file(system.file("XML/conn2.gxl",package="RBGL"), open="r") dd <- fromGXL(con) close(con) bellman.ford.sp(dd) bellman.ford.sp(dd,nodes(dd)[2])con <- file(system.file("XML/conn2.gxl",package="RBGL"), open="r") dd <- fromGXL(con) close(con) bellman.ford.sp(dd) bellman.ford.sp(dd,nodes(dd)[2])
Graph clustering based on edge betweenness centrality
betweenness.centrality.clustering(g, threshold = -1, normalize = TRUE)betweenness.centrality.clustering(g, threshold = -1, normalize = TRUE)
g |
an instance of the |
threshold |
threshold to terminate clustering process |
normalize |
boolean, when TRUE, the edge betweenness centrality is
scaled by |
To implement graph clustering based on edge betweenness centrality.
The algorithm is iterative, at each step it computes the edge betweenness
centrality and removes the edge with maximum betweenness centrality when it
is above the given threshold. When the maximum betweenness centrality
falls below the threshold, the algorithm terminates.
See documentation on Clustering algorithms in Boost Graph Library for details.
A list of
no.of.edges |
number of remaining edges after removal |
edges |
remaining edges |
edge.betweenness.centrality |
betweenness centrality of remaining edges |
Li Long <[email protected]>
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
The Boost Graph Library: User Guide and Reference Manual; by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine; (Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0-201-72914-8
brandes.betweenness.centrality
These functions return information on graph traversal by breadth and depth first search using routines from the BOOST library.
bfs(object, node, checkConn=TRUE) dfs(object, node, checkConn=TRUE)bfs(object, node, checkConn=TRUE) dfs(object, node, checkConn=TRUE)
object |
instance of class graph from Bioconductor graph class |
node |
node name where search starts; defaults to the node in first position in the node vector. |
checkConn |
logical for backwards compatibility; this parameter has no effect as of RBGL 1.7.9 and will be removed in future versions. |
These two functions are interfaces to the BOOST graph library functions for breadth first and depth first search. Both methods handle unconnected graphs by applying the algorithms over the connected components.
Cormen et al note (p 542) that 'results of depth-first search may depend upon the order in which the vertices are examined ... These different visitation orders tend not to cause problems in practice, as any DFS result can usually be used effectively, with essentially equivalent results'.
For bfs a vector of node indices in order of BFS visit.
For dfs a list of two vectors of nodes, with elements discover
(order of DFS discovery), and finish (order of DFS completion).
VJ Carey <[email protected]>
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
The Boost Graph Library: User Guide and Reference Manual; by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine; (Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0-201-72914-8
con1 <- file(system.file("XML/bfsex.gxl",package="RBGL"), open="r") dd <- fromGXL(con1) close(con1) bfs(dd, "r") bfs(dd, "s") con2 <- file(system.file("XML/dfsex.gxl",package="RBGL"), open="r") dd2 <- fromGXL(con2) close(con2) dfs(dd2, "u")con1 <- file(system.file("XML/bfsex.gxl",package="RBGL"), open="r") dd <- fromGXL(con1) close(con1) bfs(dd, "r") bfs(dd, "s") con2 <- file(system.file("XML/dfsex.gxl",package="RBGL"), open="r") dd2 <- fromGXL(con2) close(con2) dfs(dd2, "u")
Compute biconnected components for a graph
biConnComp(g) articulationPoints(g)biConnComp(g) articulationPoints(g)
g |
an instance of the |
A biconnected graph is a connected graph that remains connected when any one of its vertices, and all the edges incident on this vertex, is removed and the graph remains connected. A biconnected component of a graph is a subgraph which is biconnected. An integer label is assigned to each edge to indicate which biconnected component it's in.
A vertex in a graph is called an articulation point if removing it increases the number of connected components.
See the documentation for the Boost Graph Library for more details.
For biConnComp:
a vector whose length is no. of biconnected components, each entry is a list
of nodes that are on the same biconnected components.
For articulationPoints:
a vector of articulation points in the graph.
Li Long <[email protected]>
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
The Boost Graph Library: User Guide and Reference Manual; by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine; (Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0-201-72914-8
con <- file(system.file("XML/conn.gxl",package="RBGL"), open="r") coex <- fromGXL(con) close(con) biConnComp(coex) articulationPoints(coex)con <- file(system.file("XML/conn.gxl",package="RBGL"), open="r") coex <- fromGXL(con) close(con) biConnComp(coex) articulationPoints(coex)
boyerMyrvoldPlanarityTest description
boyerMyrvoldPlanarityTest(g)boyerMyrvoldPlanarityTest(g)
g |
instance of class graphNEL from Bioconductor graph class |
logical, TRUE if test succeeds
Li Long <[email protected]>
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
The Boost Graph Library: User Guide and Reference Manual; by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine; (Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0-201-72914-8
con <- file(system.file("XML/dijkex.gxl",package="RBGL"), open="r") coex <- fromGXL(con) edgemode(coex) = "undirected" boyerMyrvoldPlanarityTest(coex) # only shows runnability, need better casecon <- file(system.file("XML/dijkex.gxl",package="RBGL"), open="r") coex <- fromGXL(con) edgemode(coex) = "undirected" boyerMyrvoldPlanarityTest(coex) # only shows runnability, need better case
Compute betweenness centrality for an undirected graph
brandes.betweenness.centrality(g)brandes.betweenness.centrality(g)
g |
an instance of the |
Brandes.betweenness.centrality computes the betweenness centrality of
each vertex or each edge in the graph, using an algorithm by U. Brandes.
Betweenness centrality of a vertex v is calculated as follows:
N_st(v) = no. of shortest paths from s to t that pass through v,
N_st = no. of shortest paths from s to t,
betweenness centrality of v = sum(N_st(v)/N_st) for all vertices s != v != t.
Betweenness centrality of an edge is calculated similarly.
The relative betweenness centrality for a vertex is to scale the betweenness
centrality of the given vertex by 2/(n**2 - 3n + 2) where n is
the no. of vertices in the graph.
Central point dominance measures the maximum betweenness of any vertex in the graph.
See documentation on brandes betweenness centrality in Boost Graph Library for more details.
A list of
betweenness.centrality.vertices |
betweenness centrality of each vertex |
betweenness.centrality.edges |
betweenness centrality of each edge |
relative.betweenness.centrality.vertices |
relative betweenness centrality of each vertex |
dominance |
maximum betweenness of any point in the graph |
Li Long <[email protected]>
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
The Boost Graph Library: User Guide and Reference Manual; by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine; (Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0-201-72914-8
betweenness.centrality.clustering
chrobakPayneStraightLineDrawing description
chrobakPayneStraightLineDrawing(g)chrobakPayneStraightLineDrawing(g)
g |
instance of class graphNEL from Bioconductor graph class |
M. Chrobak, T. Payne, A Linear-time Algorithm for Drawing a Planar Graph on the Grid, Information Processing Letters 54: 241-246, 1995.
A matrix with rows 'x' and 'y', and columns corresponding to graph nodes.
Li Long <[email protected]>
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
The Boost Graph Library: User Guide and Reference Manual; by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine; (Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0-201-72914-8
V <- LETTERS[1:7] g <- new("graphNEL", nodes=V, edgemode="undirected") g <- addEdge(V[1+0], V[1+1], g) g <- addEdge(V[1+1], V[2+1], g) g <- addEdge(V[1+2], V[3+1], g) g <- addEdge(V[1+3], V[0+1], g) g <- addEdge(V[1+3], V[4+1], g) g <- addEdge(V[1+4], V[5+1], g) g <- addEdge(V[1+5], V[6+1], g) g <- addEdge(V[1+6], V[3+1], g) g <- addEdge(V[1+0], V[4+1], g) g <- addEdge(V[1+1], V[3+1], g) g <- addEdge(V[1+3], V[5+1], g) g <- addEdge(V[1+2], V[6+1], g) g <- addEdge(V[1+1], V[4+1], g) g <- addEdge(V[1+1], V[5+1], g) g <- addEdge(V[1+1], V[6+1], g) x3 <- chrobakPayneStraightLineDrawing(g) x3 plot(t(x3)) el = edgeL(g) for (i in seq_len(length(nodes(g)))) segments( rep(x3["x",i], length(el[[i]]$edges)), rep(x3["y",i], length(el[[i]]$edges)), x3["x", nodes(g)[el[[i]]$edges]], x3["y", nodes(g)[el[[i]]$edges]] )V <- LETTERS[1:7] g <- new("graphNEL", nodes=V, edgemode="undirected") g <- addEdge(V[1+0], V[1+1], g) g <- addEdge(V[1+1], V[2+1], g) g <- addEdge(V[1+2], V[3+1], g) g <- addEdge(V[1+3], V[0+1], g) g <- addEdge(V[1+3], V[4+1], g) g <- addEdge(V[1+4], V[5+1], g) g <- addEdge(V[1+5], V[6+1], g) g <- addEdge(V[1+6], V[3+1], g) g <- addEdge(V[1+0], V[4+1], g) g <- addEdge(V[1+1], V[3+1], g) g <- addEdge(V[1+3], V[5+1], g) g <- addEdge(V[1+2], V[6+1], g) g <- addEdge(V[1+1], V[4+1], g) g <- addEdge(V[1+1], V[5+1], g) g <- addEdge(V[1+1], V[6+1], g) x3 <- chrobakPayneStraightLineDrawing(g) x3 plot(t(x3)) el = edgeL(g) for (i in seq_len(length(nodes(g)))) segments( rep(x3["x",i], length(el[[i]]$edges)), rep(x3["y",i], length(el[[i]]$edges)), x3["x", nodes(g)[el[[i]]$edges]], x3["y", nodes(g)[el[[i]]$edges]] )
Calculate clustering coefficient for an undirected graph
clusteringCoef(g, Weighted=FALSE, vW=degree(g))clusteringCoef(g, Weighted=FALSE, vW=degree(g))
g |
an instance of the |
Weighted |
calculate weighted clustering coefficient or not |
vW |
vertex weights to use when calculating weighted clustering coefficient |
For an undirected graph G, let delta(v) be the number of triangles with
v
as a node, let tau(v) be the number of triples, i.e., paths of length 2 with
v as the center node.
Let V' be the set of nodes with degree at least 2.
Define clustering coefficient for v, c(v) = (delta(v) / tau(v)).
Define clustering coefficient for G, C(G) = sum(c(v)) / |V'|,
for all v in V'.
Define weighted clustering coefficient for g,
Cw(G) = sum(w(v) * c(v)) / sum(w(v)), for all v in V'.
Clustering coefficient for graph G.
Li Long [email protected]
Approximating Clustering Coefficient and Transitivity, T. Schank, D. Wagner, Journal of Graph Algorithms and Applications, Vol. 9, No. 2 (2005).
clusteringCoefAppr, transitivity, graphGenerator
con <- file(system.file("XML/conn.gxl",package="RBGL")) g <- fromGXL(con) close(con) cc <- clusteringCoef(g) ccw1 <- clusteringCoef(g, Weighted=TRUE) vW <- c(1, 1, 1, 1, 1,1, 1, 1) ccw2 <- clusteringCoef(g, Weighted=TRUE, vW)con <- file(system.file("XML/conn.gxl",package="RBGL")) g <- fromGXL(con) close(con) cc <- clusteringCoef(g) ccw1 <- clusteringCoef(g, Weighted=TRUE) vW <- c(1, 1, 1, 1, 1,1, 1, 1) ccw2 <- clusteringCoef(g, Weighted=TRUE, vW)
Approximate clustering coefficient for an undirected graph
clusteringCoefAppr(g, k=length(nodes(g)), Weighted=FALSE, vW=degree(g))clusteringCoefAppr(g, k=length(nodes(g)), Weighted=FALSE, vW=degree(g))
g |
an instance of the |
Weighted |
calculate weighted clustering coefficient or not |
vW |
vertex weights to use when calculating weighted clustering coefficient |
k |
parameter controls total expected runtime |
It is quite expensive to compute cluster coefficient and transitivity exactly
for a large graph by computing the number of triangles in the graph. Instead,
clusteringCoefAppr samples triples with appropriate probability, returns
the ratio between the number of existing edges and the number of samples.
MORE ABOUT CHOICE OF K.
See reference for more details.
Approximated clustering coefficient for graph g.
Li Long <[email protected]>
Approximating Clustering Coefficient and Transitivity, T. Schank, D. Wagner, Journal of Graph Algorithms and Applications, Vol. 9, No. 2 (2005).
clusteringCoef, transitivity, graphGenerator
con <- file(system.file("XML/conn.gxl",package="RBGL")) g <- fromGXL(con) close(con) k = length(nodes(g)) cc <- clusteringCoefAppr(g, k) ccw1 <- clusteringCoefAppr(g, k, Weighted=TRUE) vW <- c(1, 1, 1, 1, 1,1, 1, 1) ccw2 <- clusteringCoefAppr(g, k, Weighted=TRUE, vW)con <- file(system.file("XML/conn.gxl",package="RBGL")) g <- fromGXL(con) close(con) k = length(nodes(g)) cc <- clusteringCoefAppr(g, k) ccw1 <- clusteringCoefAppr(g, k, Weighted=TRUE) vW <- c(1, 1, 1, 1, 1,1, 1, 1) ccw2 <- clusteringCoefAppr(g, k, Weighted=TRUE, vW)
The connected components in an undirected graph are identified. If the graph is directed then the weakly connected components are identified.
connectedComp(g)connectedComp(g)
g |
graph with |
Uses a depth first search approach to identifying all the connected
components of an undirected graph. If the input, g, is a directed
graph it is first transformed to an undirected graph (using
ugraph).
A list of length equal to the number of connected components in
g. Each element of the list contains a vector of the node
labels for the nodes that are connected.
Vince Carey <[email protected]>
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
The Boost Graph Library: User Guide and Reference Manual; by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine; (Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0-201-72914-8
connComp,strongComp, ugraph, same.component
con <- file(system.file("GXL/kmstEx.gxl",package="graph"), open="r") km <- fromGXL(con) close(con) km <- graph::addNode(c("F","G","H"), km) km <- addEdge("G", "H", km, 1) km <- addEdge("H", "G", km, 1) ukm <- ugraph(km) ukm edges(ukm) connectedComp(ukm)con <- file(system.file("GXL/kmstEx.gxl",package="graph"), open="r") km <- fromGXL(con) close(con) km <- graph::addNode(c("F","G","H"), km) km <- addEdge("G", "H", km, 1) km <- addEdge("H", "G", km, 1) ukm <- ugraph(km) ukm edges(ukm) connectedComp(ukm)
Algorithm for the single-source shortest-paths problem on a weighted, directed acyclic graph (DAG)
dag.sp(g,start=nodes(g)[1])dag.sp(g,start=nodes(g)[1])
g |
instance of class graph |
start |
source node for start of paths |
These functions are interfaces to the Boost graph library C++ routines for single-source shortest-paths on a weighted directed acyclic graph. Choose appropriate shortest-path algorithms carefully based on the properties of the input graph. See documentation in Boost Graph Library for more details.
A list with elements:
distance |
The vector of distances from |
penult |
A vector of indices
(in |
start |
The start node that was supplied in the call to
|
Li Long <[email protected]>
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
The Boost Graph Library: User Guide and Reference Manual; by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine; (Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0-201-72914-8
bellman.ford.sp, dijkstra.sp, johnson.all.pairs.sp, sp.between
con <- file(system.file("XML/conn2.gxl",package="RBGL"), open="r") dd <- fromGXL(con) close(con) dag.sp(dd) dag.sp(dd,nodes(dd)[2])con <- file(system.file("XML/conn2.gxl",package="RBGL"), open="r") dd <- fromGXL(con) close(con) dag.sp(dd) dag.sp(dd,nodes(dd)[2])
dijkstra's shortest paths
dijkstra.sp(g,start=nodes(g)[1], eW=unlist(edgeWeights(g)))dijkstra.sp(g,start=nodes(g)[1], eW=unlist(edgeWeights(g)))
g |
instance of class graph |
start |
character: node name for start of path |
eW |
numeric: edge weights. |
These functions are interfaces to the Boost graph library C++ routines for Dijkstra's shortest paths.
For some graph subclasses, computing the edge weights can be expensive.
If you are calling dijkstra.sp in a loop, you can pass the edge
weights explicitly to avoid the edge weight creation cost.
A list with elements:
distance |
The vector of distances from |
penult |
A vector of indices
(in |
. For example, if the
element one of this vector has value 10, that means that the
predecessor of node 1 is node 10. The next predecessor is
found by examining penult[10].
start |
The start node that was supplied in the call to
|
VJ Carey <[email protected]>
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
The Boost Graph Library: User Guide and Reference Manual; by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine; (Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0-201-72914-8
bellman.ford.sp, dag.sp, johnson.all.pairs.sp, sp.between
con1 <- file(system.file("XML/dijkex.gxl",package="RBGL"), open="r") dd <- fromGXL(con1) close(con1) dijkstra.sp(dd) dijkstra.sp(dd,nodes(dd)[2]) con2 <- file(system.file("XML/ospf.gxl",package="RBGL"), open="r") ospf <- fromGXL(con2) close(con2) dijkstra.sp(ospf,nodes(ospf)[6])con1 <- file(system.file("XML/dijkex.gxl",package="RBGL"), open="r") dd <- fromGXL(con1) close(con1) dijkstra.sp(dd) dijkstra.sp(dd,nodes(dd)[2]) con2 <- file(system.file("XML/ospf.gxl",package="RBGL"), open="r") ospf <- fromGXL(con2) close(con2) dijkstra.sp(ospf,nodes(ospf)[6])
Compute dominator tree from a vertex in a directed graph
dominatorTree(g, start=nodes(g)[1]) lengauerTarjanDominatorTree(g, start=nodes(g)[1])dominatorTree(g, start=nodes(g)[1]) lengauerTarjanDominatorTree(g, start=nodes(g)[1])
g |
a directed graph, one instance of the |
start |
a vertex in graph |
As stated in documentation on Lengauer Tarjan dominator tree in Boost Graph Library:
A vertex u dominates a vertex v, if every path of directed graph from the entry to v must go through u.
This function builds the dominator tree for a directed graph.
Output is a vector, giving each node its immediate dominator.
Li Long <[email protected]>
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
con1 <- file(system.file("XML/dominator.gxl",package="RBGL"), open="r") g1 <- fromGXL(con1) close(con1) dominatorTree(g1) lengauerTarjanDominatorTree(g1)con1 <- file(system.file("XML/dominator.gxl",package="RBGL"), open="r") g1 <- fromGXL(con1) close(con1) dominatorTree(g1) lengauerTarjanDominatorTree(g1)
computed edge connectivity and min disconnecting set for an undirected graph
edgeConnectivity(g)edgeConnectivity(g)
g |
an instance of the |
Consider a graph G consisting of a single connected component. The edge connectivity of G is the minimum number of edges in G that can be cut to produce a graph with two (disconnected) components. The set of edges in this cut is called the minimum disconnecting set.
A list:
connectivity |
the integer describing the number of edges that must be severed to obtain two components |
minDisconSet |
a list (of length |
Vince Carey <[email protected]>
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
The Boost Graph Library: User Guide and Reference Manual; by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine; (Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0-201-72914-8
minCut, edmonds.karp.max.flow, push.relabel.max.flow
con <- file(system.file("XML/conn.gxl",package="RBGL"), open="r") coex <- fromGXL(con) close(con) edgeConnectivity(coex)con <- file(system.file("XML/conn.gxl",package="RBGL"), open="r") coex <- fromGXL(con) close(con) edgeConnectivity(coex)
edmondsMaxCardinalityMatching description
edmondsMaxCardinalityMatching(g)edmondsMaxCardinalityMatching(g)
g |
instance of class graphNEL from Bioconductor graph class |
a list with two components: a logical named 'Is max matching' and a character matrix named 'Matching' with two rows 'vertex' and 'matched vertex', entries are node labels.
Li Long <[email protected]>
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
The Boost Graph Library: User Guide and Reference Manual; by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine; (Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0-201-72914-8
V <- LETTERS[1:18] g <- new("graphNEL", nodes=V, edgemode="undirected") g <- addEdge(V[1+0], V[4+1], g); g <- addEdge(V[1+1], V[5+1], g); g <- addEdge(V[1+2], V[6+1], g); g <- addEdge(V[1+3], V[7+1], g); g <- addEdge(V[1+4], V[5+1], g); g <- addEdge(V[1+6], V[7+1], g); g <- addEdge(V[1+4], V[8+1], g); g <- addEdge(V[1+5], V[9+1], g); g <- addEdge(V[1+6], V[10+1], g); g <- addEdge(V[1+7], V[11+1], g); g <- addEdge(V[1+8], V[9+1], g); g <- addEdge(V[1+10], V[11+1], g); g <- addEdge(V[1+8], V[13+1], g); g <- addEdge(V[1+9], V[14+1], g); g <- addEdge(V[1+10], V[15+1], g); g <- addEdge(V[1+11], V[16+1], g); g <- addEdge(V[1+14], V[15+1], g); x9 <- edmondsMaxCardinalityMatching(g) x9 g <- addEdge(V[1+12], V[13+1], g); g <- addEdge(V[1+16], V[17+1], g); x10 <- edmondsMaxCardinalityMatching(g) x10V <- LETTERS[1:18] g <- new("graphNEL", nodes=V, edgemode="undirected") g <- addEdge(V[1+0], V[4+1], g); g <- addEdge(V[1+1], V[5+1], g); g <- addEdge(V[1+2], V[6+1], g); g <- addEdge(V[1+3], V[7+1], g); g <- addEdge(V[1+4], V[5+1], g); g <- addEdge(V[1+6], V[7+1], g); g <- addEdge(V[1+4], V[8+1], g); g <- addEdge(V[1+5], V[9+1], g); g <- addEdge(V[1+6], V[10+1], g); g <- addEdge(V[1+7], V[11+1], g); g <- addEdge(V[1+8], V[9+1], g); g <- addEdge(V[1+10], V[11+1], g); g <- addEdge(V[1+8], V[13+1], g); g <- addEdge(V[1+9], V[14+1], g); g <- addEdge(V[1+10], V[15+1], g); g <- addEdge(V[1+11], V[16+1], g); g <- addEdge(V[1+14], V[15+1], g); x9 <- edmondsMaxCardinalityMatching(g) x9 g <- addEdge(V[1+12], V[13+1], g); g <- addEdge(V[1+16], V[17+1], g); x10 <- edmondsMaxCardinalityMatching(g) x10
edmondsOptimumBranching description
edmondsOptimumBranching(g)edmondsOptimumBranching(g)
g |
instance of class graphNEL from Bioconductor graph class |
This is an implementation of Edmonds' algorithm to find optimum branching in a directed graph. See references for details.
A list with three elements: edgeList, weights, and nodes for the optimum branching traversal
Li Long <[email protected]>
See Edmonds' Algorithm on https://github.com/atofigh/edmonds-alg
V <- LETTERS[1:4] g <- new("graphNEL", nodes=V, edgemode="directed") g <- addEdge(V[1+0],V[1+1],g, 3) g <- addEdge(V[1+0],V[2+1],g, 1.5) g <- addEdge(V[1+0],V[3+1],g, 1.8) g <- addEdge(V[1+1],V[2+1],g, 4.3) g <- addEdge(V[1+2],V[3+1],g, 2.2) x11 <- edmondsOptimumBranching(g) x11V <- LETTERS[1:4] g <- new("graphNEL", nodes=V, edgemode="directed") g <- addEdge(V[1+0],V[1+1],g, 3) g <- addEdge(V[1+0],V[2+1],g, 1.5) g <- addEdge(V[1+0],V[3+1],g, 1.8) g <- addEdge(V[1+1],V[2+1],g, 4.3) g <- addEdge(V[1+2],V[3+1],g, 2.2) x11 <- edmondsOptimumBranching(g) x11
determine a path between two nodes in a graph,
using output of dijkstra.sp.
extractPath(s, f, pens)extractPath(s, f, pens)
s |
index of starting node in nodes vector
of the graph from which |
f |
index of ending node in nodes vector |
pens |
predecessor index vector as returned
in the |
numeric vector of indices of nodes along shortest path
Vince Carey <[email protected]>
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
The Boost Graph Library: User Guide and Reference Manual; by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine; (Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0-201-72914-8
data(FileDep) dd <- dijkstra.sp(FileDep) extractPath(1,9,dd$pen)data(FileDep) dd <- dijkstra.sp(FileDep) extractPath(1,9,dd$pen)
FileDep: a graphNEL object representing a file dependency dataset example in boost graph library
data(FileDep)data(FileDep)
an instance of graphNEL
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
The Boost Graph Library: User Guide and Reference Manual; by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine; (Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0-201-72914-8
# this is how the graph of data(FileDep) was obtained library(graph) fd <- file(system.file("XML/FileDep.gxl",package="RBGL"), open="r") show(fromGXL(fd)) if (require(Rgraphviz)) { data(FileDep) plot(FileDep) } close(fd)# this is how the graph of data(FileDep) was obtained library(graph) fd <- file(system.file("XML/FileDep.gxl",package="RBGL"), open="r") show(fromGXL(fd)) if (require(Rgraphviz)) { data(FileDep) plot(FileDep) } close(fd)
compute shortest paths for all pairs of nodes
floyd.warshall.all.pairs.sp(g)floyd.warshall.all.pairs.sp(g)
g |
graph object with edge weights given |
Compute shortest paths between every pair of vertices for a dense graph.
It works on both undirected and directed graph.
The result is given as a distance matrix. The matrix is symmetric for an
undirected graph, and asymmetric (very likely) for a directed graph.
For a sparse graph, the johnson.all.pairs.sp functions
should be used instead.
See documentation on these algorithms in Boost Graph Library for more details.
A matrix of shortest path lengths between all pairs of nodes in the graph.
Li Long <[email protected]>
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
The Boost Graph Library: User Guide and Reference Manual; by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine; (Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0-201-72914-8
con <- file(system.file("XML/conn.gxl", package="RBGL"), open="r") coex <- fromGXL(con) close(con) floyd.warshall.all.pairs.sp(coex)con <- file(system.file("XML/conn.gxl", package="RBGL"), open="r") coex <- fromGXL(con) close(con) floyd.warshall.all.pairs.sp(coex)
Compute profile for a graph
gprofile(g)gprofile(g)
g |
an instance of the |
The profile of a given graph is the sum of bandwidths for all the vertices in the graph.
See documentation on this function in Boost Graph Library for more details.
profile |
the profile of the graph |
Li Long <[email protected]>
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
The Boost Graph Library: User Guide and Reference Manual; by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine; (Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0-201-72914-8
con <- file(system.file("XML/dijkex.gxl",package="RBGL"), open="r") coex <- fromGXL(con) close(con) gprofile(coex)con <- file(system.file("XML/dijkex.gxl",package="RBGL"), open="r") coex <- fromGXL(con) close(con) gprofile(coex)
Generate an undirected graph with adjustable clustering coefficient
graphGenerator(n, d, o)graphGenerator(n, d, o)
n |
no. of nodes in the generated graph |
d |
parameter for preferential attachment |
o |
parameter for triple generation |
The graph generator works according to the prefential attachment rule. It also generates graphs with adjustable clustering coefficient. Parameter d specifies how many preferred edges a new node has. Parameter o limits how many triples to add to a new node.
See reference for details.
no. of nodes |
No. of nodes in the generated graph |
no. of edges |
No. of edges in the generated graph |
edges |
Edges in the generated graph |
Li Long <[email protected]>
Approximating Clustering Coefficient and Transitivity, T. Schank, D. Wagner, Journal of Graph Algorithms and Applications, Vol. 9, No. 2 (2005).
clusteringCoef, transitivity, clusteringCoefAppr
n <- 20 d <- 6 o <- 3 gg <- graphGenerator(n, d, o)n <- 20 d <- 6 o <- 3 gg <- graphGenerator(n, d, o)
Compute highly connected subgraphs for an undirected graph
highlyConnSG(g, sat=3, ldv=c(3,2,1))highlyConnSG(g, sat=3, ldv=c(3,2,1))
g |
an instance of the |
sat |
singleton adoption threshold, positive integer |
ldv |
heuristics to remove lower degree vertice, a decreasing sequence of positive integer |
A graph G with n vertices is highly connected if its connectivity k(G) > n/2. The HCS algorithm partitions a given graph into a set of highly connected subgraphs, by using minimum-cut algorithm recursively. To improve performance, the approach is refined by adopting singletons, removing low degree vertices and merging clusters.
On singleton adoption: after each round of partition, some highly connected subgraphs could be singletons (i.e., a subgraph contains only one node). To reduce the number of singletons, therefore reduce number of clusters, we try to get "normal" subgraphs to "adopt" them. If a singleton, s, has n neighbours in a highly connected subgraph c, and n > sat, we add s to c. To adapt to the modified subgraphs, this adoption process is repeated until no further such adoption.
On lower degree vertices: when the graph has low degree vertices, minimum-cut algorithm will just repeatedly separate these vertices from the rest. To reduce such expensive and non-informative computation, we "remove" these low degree vertices first before applying minimum-cut algorithm. Given ldv = (d1, d2, ..., dp), (d[i] > d[i+1] > 0), we repeat the following (i from 1 to p): remove all the highly-connected-subgraph found so far; remove vertices with degrees < di; find highly-connected-subgraphs; perform singleton adoptions.
The Boost implementation does not support self-loops, therefore we
signal an error and suggest that users remove self-loops using the
function removeSelfLoops function. This change does affect
degree, but the original article makes no specific reference to self-loops.
A list of clusters, each is given as vertices in the graph.
Li Long <[email protected]>
A Clustering Algorithm based on Graph Connectivity by E. Hartuv, R. Shamir, 1999.
edgeConnectivity, minCut, removeSelfLoops
con <- file(system.file("XML/hcs.gxl",package="RBGL")) coex <- fromGXL(con) close(con) highlyConnSG(coex)con <- file(system.file("XML/hcs.gxl",package="RBGL")) coex <- fromGXL(con) close(con) highlyConnSG(coex)
Compute connected components for an undirected graph
init.incremental.components(g) incremental.components(g) same.component(g, node1, node2)init.incremental.components(g) incremental.components(g) same.component(g, node1, node2)
g |
an instance of the |
node1 |
one vertex of the given graph |
node2 |
another vertex of the given graph |
This family of functions work together to calculate the connected components of an undirected graph. The algorithm is based on the disjoint-sets. It works where the graph is growing by adding new edges. Call "init.incremental.components" to initialize the calculation on a new graph. Call "incremental.components" to re-calculate connected components after growing the graph. Call "same.component" to learn if two given vertices are in the same connected components. Currently, the codes can only handle ONE incremental graph at a time. When you start working on another graph by calling "init.incremental.components", the disjoint-sets info on the previous graph is lost. See documentation on Incremental Connected Components in Boost Graph Library for more details.
Output from init.incremental.components is a list of component numbers for each vertex in the graph.
Output from incremental.components is a list of component numbers for each vertex in the graph.
Output from same.component is true if both nodes are in the same connected component, otherwise it's false.
Li Long <[email protected]>
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
The Boost Graph Library: User Guide and Reference Manual; by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine; (Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0-201-72914-8
connComp, connectedComp, strongComp
con <- file(system.file("XML/conn2.gxl",package="RBGL"), open="r") coex <- fromGXL(con) close(con) init.incremental.components(coex) incremental.components(coex) v1 <- 1 v2 <- 5 same.component(coex, v1, v2)con <- file(system.file("XML/conn2.gxl",package="RBGL"), open="r") coex <- fromGXL(con) close(con) init.incremental.components(coex) incremental.components(coex) v1 <- 1 v2 <- 5 same.component(coex, v1, v2)
Decide if a graph is triangulated
is.triangulated(g)is.triangulated(g)
g |
an instance of the |
An undirected graph G = (V, E) is triangulated (i.e. chordal) if all cycles [v1, v2, ..., vk] of length 4 or more have a chord, i.e., an edge [vi, vj] with j != i +/- 1 (mod k)
An equivalent definition of chordal graphs is:
G is chordal iff either G is an empty graph, or there is an v in V such that
the neighborhood of v (i.e., v and its adjacent nodes) forms a clique, and
recursively, G-v is chordal
The return value is TRUE if g is triangulated and FALSE
otherwise. An error is thrown if the graph is not undirected; you might use
ugraph to compute the underlying graph.
Li Long <[email protected]>
Combinatorial Optimization: algorithms and complexity (p. 403) by C. H. Papadimitriou, K. Steiglitz
con1 <- file(system.file("XML/conn.gxl",package="RBGL"), open="r") coex <- fromGXL(con1) close(con1) is.triangulated(coex) con2 <- file(system.file("XML/hcs.gxl",package="RBGL"), open="r") coex <- fromGXL(con2) close(con2) is.triangulated(coex)con1 <- file(system.file("XML/conn.gxl",package="RBGL"), open="r") coex <- fromGXL(con1) close(con1) is.triangulated(coex) con2 <- file(system.file("XML/hcs.gxl",package="RBGL"), open="r") coex <- fromGXL(con2) close(con2) is.triangulated(coex)
isKuratowskiSubgraph description
isKuratowskiSubgraph(g)isKuratowskiSubgraph(g)
g |
instance of class graphNEL from Bioconductor graph class |
a list with three elements: 'Is planar' (logical), 'Is there a Kuratowski subgraph' (logical), and a two-row character matrix 'Edges of Kuratowski subgraph' with rows 'from' and 'two' and node names as entries.
Li Long <[email protected]>
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
The Boost Graph Library: User Guide and Reference Manual; by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine; (Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0-201-72914-8
V <- LETTERS[1:6] g <- new("graphNEL", nodes=V, edgemode="undirected") g <- addEdge(V[1+0], V[1+1], g) g <- addEdge(V[1+0], V[2+1], g) g <- addEdge(V[1+0], V[3+1], g) g <- addEdge(V[1+0], V[4+1], g) g <- addEdge(V[1+0], V[5+1], g) g <- addEdge(V[1+1], V[2+1], g) g <- addEdge(V[1+1], V[3+1], g) g <- addEdge(V[1+1], V[4+1], g) g <- addEdge(V[1+1], V[5+1], g) g <- addEdge(V[1+2], V[3+1], g) g <- addEdge(V[1+2], V[4+1], g) g <- addEdge(V[1+2], V[5+1], g) g <- addEdge(V[1+3], V[4+1], g) g <- addEdge(V[1+3], V[5+1], g) g <- addEdge(V[1+4], V[5+1], g) x4 <- isKuratowskiSubgraph(g) x4V <- LETTERS[1:6] g <- new("graphNEL", nodes=V, edgemode="undirected") g <- addEdge(V[1+0], V[1+1], g) g <- addEdge(V[1+0], V[2+1], g) g <- addEdge(V[1+0], V[3+1], g) g <- addEdge(V[1+0], V[4+1], g) g <- addEdge(V[1+0], V[5+1], g) g <- addEdge(V[1+1], V[2+1], g) g <- addEdge(V[1+1], V[3+1], g) g <- addEdge(V[1+1], V[4+1], g) g <- addEdge(V[1+1], V[5+1], g) g <- addEdge(V[1+2], V[3+1], g) g <- addEdge(V[1+2], V[4+1], g) g <- addEdge(V[1+2], V[5+1], g) g <- addEdge(V[1+3], V[4+1], g) g <- addEdge(V[1+3], V[5+1], g) g <- addEdge(V[1+4], V[5+1], g) x4 <- isKuratowskiSubgraph(g) x4
Compute isomorphism from vertices in one graph to those in another graph
isomorphism(g1, g2)isomorphism(g1, g2)
g1 |
one instance of the |
g2 |
one instance of the |
As stated in documentation on isomorphism in Boost Graph Library: An isomorphism is a 1-to-1 mapping of the vertices in one graph to the vertices of another graph such that adjacency is preserved. Another words, given graphs G1 = (V1,E1) and G2 = (V2,E2) an isomorphism is a function f such that for all pairs of vertices a,b in V1, edge (a,b) is in E1 if and only if edge (f(a),f(b)) is in E2.
Output is true if there exists an isomorphism between g1 and g2, otherwise it's false.
Li Long <[email protected]>
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
The Boost Graph Library: User Guide and Reference Manual; by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine; (Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0-201-72914-8
con1 <- file(system.file("XML/dijkex.gxl",package="RBGL"), open="r") g1 <- fromGXL(con1) close(con1) con2 <- file(system.file("XML/conn2.gxl",package="RBGL"), open="r") g2 <- fromGXL(con2) close(con2) isomorphism(g1, g2)con1 <- file(system.file("XML/dijkex.gxl",package="RBGL"), open="r") g1 <- fromGXL(con1) close(con1) con2 <- file(system.file("XML/conn2.gxl",package="RBGL"), open="r") g2 <- fromGXL(con2) close(con2) isomorphism(g1, g2)
isStraightLineDrawing description
isStraightLineDrawing(g, drawing)isStraightLineDrawing(g, drawing)
g |
instance of class graphNEL from Bioconductor graph class |
drawing |
coordinates of node positions |
logical, TRUE if drawing is a straight line layout for g
Li Long <[email protected]>
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
The Boost Graph Library: User Guide and Reference Manual; by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine; (Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0-201-72914-8
V <- LETTERS[1:7] g <- new("graphNEL", nodes=V, edgemode="undirected") g <- addEdge(V[1+0], V[1+1], g) g <- addEdge(V[1+1], V[2+1], g) g <- addEdge(V[1+2], V[3+1], g) g <- addEdge(V[1+3], V[0+1], g) g <- addEdge(V[1+3], V[4+1], g) g <- addEdge(V[1+4], V[5+1], g) g <- addEdge(V[1+5], V[6+1], g) g <- addEdge(V[1+6], V[3+1], g) g <- addEdge(V[1+0], V[4+1], g) g <- addEdge(V[1+1], V[3+1], g) g <- addEdge(V[1+3], V[5+1], g) g <- addEdge(V[1+2], V[6+1], g) g <- addEdge(V[1+1], V[4+1], g) g <- addEdge(V[1+1], V[5+1], g) g <- addEdge(V[1+1], V[6+1], g) x3 <- chrobakPayneStraightLineDrawing(g) x8 <- isStraightLineDrawing(g, x3) x8V <- LETTERS[1:7] g <- new("graphNEL", nodes=V, edgemode="undirected") g <- addEdge(V[1+0], V[1+1], g) g <- addEdge(V[1+1], V[2+1], g) g <- addEdge(V[1+2], V[3+1], g) g <- addEdge(V[1+3], V[0+1], g) g <- addEdge(V[1+3], V[4+1], g) g <- addEdge(V[1+4], V[5+1], g) g <- addEdge(V[1+5], V[6+1], g) g <- addEdge(V[1+6], V[3+1], g) g <- addEdge(V[1+0], V[4+1], g) g <- addEdge(V[1+1], V[3+1], g) g <- addEdge(V[1+3], V[5+1], g) g <- addEdge(V[1+2], V[6+1], g) g <- addEdge(V[1+1], V[4+1], g) g <- addEdge(V[1+1], V[5+1], g) g <- addEdge(V[1+1], V[6+1], g) x3 <- chrobakPayneStraightLineDrawing(g) x8 <- isStraightLineDrawing(g, x3) x8
compute shortest path distance matrix for all pairs of nodes
johnson.all.pairs.sp(g)johnson.all.pairs.sp(g)
g |
graph object for which edgeMatrix and edgeWeights are defined |
Uses BGL algorithm.
matrix of shortest path lengths, read from row node to col node
Vince Carey <[email protected]>
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
The Boost Graph Library: User Guide and Reference Manual; by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine; (Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0-201-72914-8
bellman.ford.sp, dag.sp, dijkstra.sp, sp.between
con <- file(system.file("dot/joh.gxl", package="RBGL"), open="r") z <- fromGXL(con) close(con) johnson.all.pairs.sp(z)con <- file(system.file("dot/joh.gxl", package="RBGL"), open="r") z <- fromGXL(con) close(con) johnson.all.pairs.sp(z)
Find all the k-cliques in an undirected graph
kCliques(g)kCliques(g)
g |
an instance of the |
Notice that there are different definitions of k-clique in different context.
In computer science, a k-clique of a graph is a clique, i.e., a complete subgraph, of k nodes.
In Social Network Analysis, a k-clique in a graph is a subgraph where the distance between any two nodes is no greater than k.
Here we take the definition in Social Network Analysis.
Let D be a matrix, D[i][j] is the shortest path from node i to node j. Algorithm is outlined as following: (1) use Johnson's algorithm to fill D; let N = max(D[i][j]) for all i, j; (2) each edge is a 1-clique by itself; (3) for k = 2, ..., N, try to expand each (k-1)-clique to k-clique: (3.1) consider a (k-1)-clique the current k-clique KC; (3.2) repeat the following: if for all nodes j in KC, D[v][j] <= k, add node v to KC; (3.3) eliminate duplicates; (4) the whole graph is N-clique.
A list of length N; k-th entry (k = 1, ..., N) is a list of all the k-cliques in graph g.
Li Long <[email protected]>
Social Network Analysis: Methods and Applications. By S. Wasserman and K. Faust, pp. 258.
con <- file(system.file("XML/snacliqueex.gxl",package="RBGL")) coex <- fromGXL(con) close(con) kCliques(coex)con <- file(system.file("XML/snacliqueex.gxl",package="RBGL")) coex <- fromGXL(con) close(con) kCliques(coex)
Find all the k-cores in a graph
kCores(g, EdgeType=c("in", "out"))kCores(g, EdgeType=c("in", "out"))
g |
an instance of the |
EdgeType |
what types of edges to be considered when |
A k-core in a graph is a subgraph where each node is adjacent to at least a minimum number, k, of the other nodes in the subgraph.
A k-core in a graph may not be connected.
The core number for each node is the highest k-core this node is in. A node in a k-core will be, by definition, in a (k-1)-core.
The implementation is based on the algorithm by V. Batagelj and M. Zaversnik, 2002.
The example snacoreex.gxl is in the paper by V. Batagelj and M. Zaversnik, 2002.
A vector of the core numbers for all the nodes in g.
Li Long <[email protected]>
Social Network Analysis: Methods and Applications. By S. Wasserman and K. Faust, pp. 266. An O(m) Algorithm for Cores decomposition of networks, by V. Batagelj and M. Zaversnik, 2002.
con1 <- file(system.file("XML/snacoreex.gxl",package="RBGL")) kcoex <- fromGXL(con1) close(con1) kCores(kcoex) con2 <- file(system.file("XML/conn2.gxl",package="RBGL")) kcoex2 <- fromGXL(con2) close(con2) kCores(kcoex2) kCores(kcoex2, "in") kCores(kcoex2, "out")con1 <- file(system.file("XML/snacoreex.gxl",package="RBGL")) kcoex <- fromGXL(con1) close(con1) kCores(kcoex) con2 <- file(system.file("XML/conn2.gxl",package="RBGL")) kcoex2 <- fromGXL(con2) close(con2) kCores(kcoex2) kCores(kcoex2, "in") kCores(kcoex2, "out")
Find all the lambda-sets in an undirected graph
lambdaSets(g)lambdaSets(g)
g |
an instance of the |
From reference (1), p. 270: A set of nodes is a lambda-set if any pair of nodes in the lambda set has larger edge connectivity than any pair of nodes consisting of one node from within the lamda set and a second node from outside the lamda set.
As stated in reference (2), a lambda set is a maximal subset of nodes who have more edge-independent paths connecting them to each other than to outsiders.
A lambda set could be characterized by the minimum edge connectivity k
among its members, and could be called lambda-k sets.
Let N be maximum edge connectivity of graph g,
we output all the lambda-k set for all k = 1, ..., N.
Maximum edge connectivity, N, of the graph g, and
A list of length N; k-th entry (k = 1, ..., N) is a list of all the lambda-k
sets in graph g.
Li Long <[email protected]>
(1) Social Network Analysis: Methods and Applications. By S. Wasserman and K. Faust, pp. 269. (2) LS sets, lambda sets and other cohesive subsets. By S. P. Borgatti, M. G. Everett, P. R. Shirey, Social Networks 12 (1990) p. 337-357
con <- file(system.file("XML/snalambdaex.gxl",package="RBGL")) coex <- fromGXL(con) close(con) lambdaSets(coex)con <- file(system.file("XML/snalambdaex.gxl",package="RBGL")) coex <- fromGXL(con) close(con) lambdaSets(coex)
Layout an undirected graph in 2D – suspended june 16 2012
circleLayout(g, radius=1) # does not compile with boost 1.49 kamadaKawaiSpringLayout( g, edge_or_side=1, es_length=1 ) fruchtermanReingoldForceDirectedLayout(g, width=1, height=1) randomGraphLayout(g, minX=0, maxX=1, minY=0, maxY=1)circleLayout(g, radius=1) # does not compile with boost 1.49 kamadaKawaiSpringLayout( g, edge_or_side=1, es_length=1 ) fruchtermanReingoldForceDirectedLayout(g, width=1, height=1) randomGraphLayout(g, minX=0, maxX=1, minY=0, maxY=1)
g |
an instance of the |
radius |
radius of a regular n-polygon |
edge_or_side |
boolean indicating the length is for an edge or for a side, default is for an edge |
es_length |
the length of an edge or a side for layout |
width |
the width of the dislay area, all x coordinates fall in [-width/2, width/2] |
height |
the height of the display area, all y coordinates fall in [-height/2, height/2] |
minX |
minimum x coordinate |
maxX |
maximum x coordinate |
minY |
minimum y coordinate |
maxY |
maximum y coordinate |
If you want to simply draw a graph, you should consider using package
Rgraphviz. The layout options in package Rgraphviz: neato,
circo and fdp, correspond to kamadaKawaiSpringLayout,
circleLayout and fruchtermanReingoldForceDirectedLayout,
respectively.
Function circleLayout layouts the graph with the vertices at the points
of a regular n-polygon. The distance from the center of the polygon to each
point is determined by the radius parameter.
Function kamadaKawaiSpringLayout provides Kamada-Kawai spring layout for
connected, undirected graphs. User provides either the unit length e of an
edge in the layout or the length of a side s of the display area.
Function randomGraphLayout places the points of the graph at random locations.
Function fruchtermanReingoldForceDirectedLayout performs layout of
unweighted, undirected graphs. It's a force-directed algorithm. The BGL
implementation doesn't handle disconnected graphs very well, since it doesn't
explicitly give each connected component a region proportional to its size.
See documentation on this function in Boost Graph Library for more details.
A (2 x n) matrix, where n is the number of nodes in the graph, each column gives the (x, y)-coordinates for the corresponding node.
Li Long <[email protected]>
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
The Boost Graph Library: User Guide and Reference Manual; by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine; (Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0-201-72914-8
## Not run: con <- file(system.file("XML/conn.gxl",package="RBGL"), open="r") coex <- fromGXL(con) close(con) coex <- ugraph(coex) circleLayout(coex) kamadaKawaiSpringLayout(coex) randomGraphLayout(coex) fruchtermanReingoldForceDirectedLayout(coex, 10, 10) ## End(Not run)## Not run: con <- file(system.file("XML/conn.gxl",package="RBGL"), open="r") coex <- fromGXL(con) close(con) coex <- ugraph(coex) circleLayout(coex) kamadaKawaiSpringLayout(coex) randomGraphLayout(coex) fruchtermanReingoldForceDirectedLayout(coex, 10, 10) ## End(Not run)
makeBiconnectedPlanar description
makeBiconnectedPlanar(g)makeBiconnectedPlanar(g)
g |
instance of class graphNEL from Bioconductor graph class |
From
http://www.boost.org/doc/libs/1_49_0/libs/graph/doc/planar_graphs.html
An undirected graph is connected if, for any two vertices u and v, there's a path from u to v. An undirected graph is biconnected if it is connected and it remains connected even if any single vertex is removed. Finally, a planar graph is maximal planar (also called triangulated) if no additional edge (with the exception of self-loops and parallel edges) can be added to it without creating a non-planar graph. Any maximal planar simple graph on n > 2 vertices has exactly 3n - 6 edges and 2n - 4 faces, a consequence of Euler's formula. If a planar graph isn't connected, isn't biconnected, or isn't maximal planar, there is some set of edges that can be added to the graph to make it satisfy any of those three properties while preserving planarity. Many planar graph drawing algorithms make at least one of these three assumptions about the input graph, so there are functions in the Boost Graph Library that can help:
make_connected adds a minimal set of edges to an undirected graph to make it connected.
make_biconnected_planar adds a set of edges to a connected, undirected planar graph to make it biconnected while preserving planarity.
make_maximal_planar adds a set of edges to a biconnected, undirected planar graph to make it maximal planar.
The function documented here implements the second approach.
A list with two elements: 'Is planar' is a logical indicating achievement of planarity, and 'new graph', a graphNEL instance that is biconnected and planar.
Li Long <[email protected]>
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
The Boost Graph Library: User Guide and Reference Manual; by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine; (Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0-201-72914-8
V <- LETTERS[1:11] g <- new("graphNEL", nodes=V, edgemode="undirected") g <- addEdge(V[1+0], V[1+1], g) g <- addEdge(V[1+2], V[3+1], g) g <- addEdge(V[1+3], V[0+1], g) g <- addEdge(V[1+3], V[4+1], g) g <- addEdge(V[1+4], V[5+1], g) g <- addEdge(V[1+5], V[3+1], g) g <- addEdge(V[1+5], V[6+1], g) g <- addEdge(V[1+6], V[7+1], g) g <- addEdge(V[1+7], V[8+1], g) g <- addEdge(V[1+8], V[5+1], g) g <- addEdge(V[1+8], V[9+1], g) g <- addEdge(V[1+0], V[10+1], g) x6 <- makeBiconnectedPlanar(g) x6V <- LETTERS[1:11] g <- new("graphNEL", nodes=V, edgemode="undirected") g <- addEdge(V[1+0], V[1+1], g) g <- addEdge(V[1+2], V[3+1], g) g <- addEdge(V[1+3], V[0+1], g) g <- addEdge(V[1+3], V[4+1], g) g <- addEdge(V[1+4], V[5+1], g) g <- addEdge(V[1+5], V[3+1], g) g <- addEdge(V[1+5], V[6+1], g) g <- addEdge(V[1+6], V[7+1], g) g <- addEdge(V[1+7], V[8+1], g) g <- addEdge(V[1+8], V[5+1], g) g <- addEdge(V[1+8], V[9+1], g) g <- addEdge(V[1+0], V[10+1], g) x6 <- makeBiconnectedPlanar(g) x6
makeConnected description
makeConnected(g)makeConnected(g)
g |
instance of class graphNEL from Bioconductor graph class |
a graphNEL instance with a minimal set of edges added to achieve connectedness.
an instance of graphNEL
Li Long <[email protected]>
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
The Boost Graph Library: User Guide and Reference Manual; by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine; (Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0-201-72914-8
V <- LETTERS[1:11] g <- new("graphNEL", nodes=V, edgemode="undirected") g <- addEdge(V[1+0], V[1+1], g) g <- addEdge(V[1+2], V[3+1], g) g <- addEdge(V[1+3], V[4+1], g) g <- addEdge(V[1+5], V[6+1], g) g <- addEdge(V[1+6], V[7+1], g) g <- addEdge(V[1+8], V[9+1], g) g <- addEdge(V[1+9], V[10+1], g) g <- addEdge(V[1+10], V[8+1], g) x5 <- makeConnected(g) x5V <- LETTERS[1:11] g <- new("graphNEL", nodes=V, edgemode="undirected") g <- addEdge(V[1+0], V[1+1], g) g <- addEdge(V[1+2], V[3+1], g) g <- addEdge(V[1+3], V[4+1], g) g <- addEdge(V[1+5], V[6+1], g) g <- addEdge(V[1+6], V[7+1], g) g <- addEdge(V[1+8], V[9+1], g) g <- addEdge(V[1+9], V[10+1], g) g <- addEdge(V[1+10], V[8+1], g) x5 <- makeConnected(g) x5
makeMaximalPlanar description
makeMaximalPlanar(g)makeMaximalPlanar(g)
g |
instance of class graphNEL from Bioconductor graph class |
see http://www.boost.org/doc/libs/1_49_0/libs/graph/doc/planar_graphs.html
a list with two elements, 'Is planar:', a logical indicating state of graph, and 'new graph', a graphNEL instance
Li Long <[email protected]>
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
The Boost Graph Library: User Guide and Reference Manual; by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine; (Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0-201-72914-8
V <- LETTERS[1:10] g <- new("graphNEL", nodes=V, edgemode="undirected") g <- addEdge(V[1+0], V[1+1], g) g <- addEdge(V[1+1], V[2+1], g) g <- addEdge(V[1+2], V[3+1], g) g <- addEdge(V[1+3], V[4+1], g) g <- addEdge(V[1+4], V[5+1], g) g <- addEdge(V[1+5], V[6+1], g) g <- addEdge(V[1+6], V[7+1], g) g <- addEdge(V[1+7], V[8+1], g) g <- addEdge(V[1+8], V[9+1], g) x7 <- makeMaximalPlanar(g) x7V <- LETTERS[1:10] g <- new("graphNEL", nodes=V, edgemode="undirected") g <- addEdge(V[1+0], V[1+1], g) g <- addEdge(V[1+1], V[2+1], g) g <- addEdge(V[1+2], V[3+1], g) g <- addEdge(V[1+3], V[4+1], g) g <- addEdge(V[1+4], V[5+1], g) g <- addEdge(V[1+5], V[6+1], g) g <- addEdge(V[1+6], V[7+1], g) g <- addEdge(V[1+7], V[8+1], g) g <- addEdge(V[1+8], V[9+1], g) x7 <- makeMaximalPlanar(g) x7
Compute max flow for a directed graph
edmonds.karp.max.flow(g, source, sink) push.relabel.max.flow(g, source, sink) kolmogorov.max.flow(g, source, sink)edmonds.karp.max.flow(g, source, sink) push.relabel.max.flow(g, source, sink) kolmogorov.max.flow(g, source, sink)
g |
an instance of the |
source |
node name (character) or node number (int) for the source of the flow |
sink |
node name (character) or node number (int) for the sink of the flow |
Given a directed graph G=(V, E) of a single connected component with a vertex
source and a vertex sink. Each arc has a positive real valued
capacity, currently it's equivalent to the weight of the arc. The flow of the
network is the net flow entering the vertex sink. The maximum flow
problem is to determine the maximum possible value for the flow to the
sink and the corresponding flow values for each arc.
See documentation on these algorithms in Boost Graph Library for more details.
A list of
maxflow |
the max flow from |
edges |
the nodes of the arcs with non-zero capacities |
flows |
the flow values of the arcs with non-zero capacities |
Li Long <[email protected]>
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
The Boost Graph Library: User Guide and Reference Manual; by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine; (Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0-201-72914-8
con <- file(system.file("XML/dijkex.gxl",package="RBGL"), open="r") g <- fromGXL(con) close(con) ans1 <- edmonds.karp.max.flow(g, "B", "D") ans2 <- edmonds.karp.max.flow(g, 3, 2) # 3 and 2 equivalent to "C" and "B" ans3 <- push.relabel.max.flow(g, 2, 4) # 2 and 4 equivalent to "B" and "D" ans4 <- push.relabel.max.flow(g, "C", "B") # error in the following now, 14 june 2014 #ans5 <- kolmogorov.max.flow(g, "B", "D") #ans6 <- kolmogorov.max.flow(g, 3, 2)con <- file(system.file("XML/dijkex.gxl",package="RBGL"), open="r") g <- fromGXL(con) close(con) ans1 <- edmonds.karp.max.flow(g, "B", "D") ans2 <- edmonds.karp.max.flow(g, 3, 2) # 3 and 2 equivalent to "C" and "B" ans3 <- push.relabel.max.flow(g, 2, 4) # 2 and 4 equivalent to "B" and "D" ans4 <- push.relabel.max.flow(g, "C", "B") # error in the following now, 14 june 2014 #ans5 <- kolmogorov.max.flow(g, "B", "D") #ans6 <- kolmogorov.max.flow(g, 3, 2)
Find all the cliques in a graph
maxClique(g, nodes=NULL, edgeMat=NULL)maxClique(g, nodes=NULL, edgeMat=NULL)
g |
an instance of the |
nodes |
vector of node names, to be supplied if |
edgeMat |
2 x p matrix with indices of edges in nodes, one-based, only to be supplied if codeg is not |
Notice the maximum clique problem is NP-complete, which means it cannot be solved by any known polynomial algorithm.
We implemented the algorithm by C. Bron and J. Kerbosch,
It is an error to supply both g and either of the other arguments.
If g is not supplied, no checking of the consistency of
nodes and edgeMat is performed.
maxClique |
list of all cliques in |
Li Long <[email protected]>
Finding all cliques of an undirected graph, by C. Bron and J. Kerbosch, Communication of ACM, Sept 1973, Vol 16, No. 9.
con1 <- file(system.file("XML/conn.gxl",package="RBGL"), open="r") coex <- fromGXL(con1) close(con1) maxClique(coex) con2 <- file(system.file("XML/hcs.gxl",package="RBGL"), open="r") coex <- fromGXL(con2) close(con2) maxClique(coex)con1 <- file(system.file("XML/conn.gxl",package="RBGL"), open="r") coex <- fromGXL(con1) close(con1) maxClique(coex) con2 <- file(system.file("XML/hcs.gxl",package="RBGL"), open="r") coex <- fromGXL(con2) close(con2) maxClique(coex)
maximumCycleRatio description
maximumCycleRatio(g)maximumCycleRatio(g)
g |
instance of class graphNEL from Bioconductor graph class |
NOT IMPLEMENTED
A list with message indicating not implemented
Li Long <[email protected]>
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
The Boost Graph Library: User Guide and Reference Manual; by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine; (Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0-201-72914-8
Compute min-cut for an undirected graph
minCut(g)minCut(g)
g |
an instance of the |
Given an undirected graph G=(V, E) of a single connected component, a cut is a partition of the set of vertices into two non-empty subsets S and V-S, a cost is the number of edges that are incident on one vertex in S and one vertex in V-S. The min-cut problem is to find a cut (S, V-S) of minimum cost.
For simplicity, the returned subset S is the smaller of the two subsets.
A list of
mincut |
the number of edges to be severed to obtain the minimum cut |
S |
the smaller subset of vertices in the minimum cut |
V-S |
the other subset of vertices in the minimum cut |
Li Long <[email protected]>
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
The Boost Graph Library: User Guide and Reference Manual; by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine; (Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0-201-72914-8
con <- file(system.file("XML/conn.gxl",package="RBGL"), open="r") coex <- fromGXL(con) close(con) minCut(coex)con <- file(system.file("XML/conn.gxl",package="RBGL"), open="r") coex <- fromGXL(con) close(con) minCut(coex)
minimumCycleRatio description
minimumCycleRatio(g)minimumCycleRatio(g)
g |
instance of class graphNEL from Bioconductor graph class |
Not yet implemented.
a list with message
Li Long <[email protected]>
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
The Boost Graph Library: User Guide and Reference Manual; by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine; (Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0-201-72914-8
compute the minimum spanning tree (MST) for a graph and return a representation in matrices
mstree.kruskal(x)mstree.kruskal(x)
x |
instance of class graph |
calls to kruskal minimum spanning tree algorithm of Boost graph library
a list
edgeList |
a matrix m of dimension 2 by number of edges in the MST, with m[i,j] the jth node in edge i |
weights |
a vector of edge weights corresponding to the
columns of |
nodes |
the vector of nodes of the input graph |
VJ Carey <[email protected]>
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
The Boost Graph Library: User Guide and Reference Manual; by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine; (Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0-201-72914-8
con1 <- file(system.file("XML/kmstEx.gxl",package="RBGL"), open="r") km <- fromGXL(con1) close(con1) mstree.kruskal(km) edgeData(km, "B", "D", "weight") <- 1.1 edgeData(km, "B", "E", "weight") <- .95 mstree.kruskal(km) con2 <- file(system.file("XML/telenet.gxl",package="RBGL"), open="r") km2 <- fromGXL(con2) close(con2) m <- mstree.kruskal(km2) print(sum(m[[2]]))con1 <- file(system.file("XML/kmstEx.gxl",package="RBGL"), open="r") km <- fromGXL(con1) close(con1) mstree.kruskal(km) edgeData(km, "B", "D", "weight") <- 1.1 edgeData(km, "B", "E", "weight") <- .95 mstree.kruskal(km) con2 <- file(system.file("XML/telenet.gxl",package="RBGL"), open="r") km2 <- fromGXL(con2) close(con2) m <- mstree.kruskal(km2) print(sum(m[[2]]))
Compute minimum spanning tree for an undirected graph
mstree.prim(g)mstree.prim(g)
g |
an instance of the |
This is Prim's algorithm for solving the minimum spanning tree problem for an undirected graph with weighted edges.
See documentations on this function in Boost Graph Library for more details.
A list of
edges |
the edges that form the minimum spanning tree |
weights |
the total weight of the minimum spanning tree |
Li Long <[email protected]>
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
The Boost Graph Library: User Guide and Reference Manual; by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine; (Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0-201-72914-8
con <- file(system.file("XML/conn2.gxl",package="RBGL")) coex <- fromGXL(con) close(con) mstree.prim(coex)con <- file(system.file("XML/conn2.gxl",package="RBGL")) coex <- fromGXL(con) close(con) mstree.prim(coex)
Compute vertex ordering for an undirected graph
cuthill.mckee.ordering(g) minDegreeOrdering(g, delta=0) sloan.ordering(g, w1=1, w2=2)cuthill.mckee.ordering(g) minDegreeOrdering(g, delta=0) sloan.ordering(g, w1=1, w2=2)
g |
an instance of the |
delta |
Multiple elimination control variable. If it is larger than or equal to zero then multiple elimination is enabled. The value of delta specifies the difference between the minimum degree and the degree of vertices that are to be eliminated. |
w1 |
First heuristic weight for the Sloan algorithm. |
w2 |
Second heuristic weight for the Sloan algorithm. |
The following details were obtained from the documentation of these algorithms in Boost Graph Library and readers are referred their for even more detail. The goal of the Cuthill-Mckee (and reverse Cuthill-Mckee) ordering algorithm is to reduce the bandwidth of a graph by reordering the indices assigned to each vertex.
The minimum degree ordering algorithm is a fill-in reduction matrix reordering algorithm.
The goal of the Sloan ordering algorithm is to reduce the profile and the wavefront of a graph by reordering the indices assigned to each vertex.
The goal of the King ordering algorithm is to reduce the bandwidth of a graph by reordering the indices assigned to each vertex.
cuthill.mckee.ordering |
returns a list with elements: |
reverse cuthill.mckee.ordering |
the vertices in the new ordering |
original bandwidth |
bandwidth before reordering vertices |
new bandwidth |
bandwidth after reordering of vertices |
minDegreeOrdering |
return a list with elements: |
inverse_permutation |
the new vertex ordering, given as the mapping from the new indices to the old indices |
permutation |
the new vertex ordering, given as the mapping from the old indices to the new indices |
sloan.ordering |
returns a list with elements: |
sloan.ordering |
the vertices in the new ordering |
bandwidth |
bandwidth of the graph after reordering |
profile |
profile of the graph after reordering |
maxWavefront |
maxWavefront of the graph after reordering |
aver.wavefront |
aver.wavefront of the graph after reordering |
rms.wavefront |
rms.wavefront of the graph after reordering |
Li Long <[email protected]>
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
The Boost Graph Library: User Guide and Reference Manual; by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine; (Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0-201-72914-8
con <- file(system.file("XML/dijkex.gxl",package="RBGL"), open="r") coex <- fromGXL(con) close(con) coex <- ugraph(coex) cuthill.mckee.ordering(coex) minDegreeOrdering(coex) sloan.ordering(coex)con <- file(system.file("XML/dijkex.gxl",package="RBGL"), open="r") coex <- fromGXL(con) close(con) coex <- ugraph(coex) cuthill.mckee.ordering(coex) minDegreeOrdering(coex) sloan.ordering(coex)
planarCanonicalOrdering description
planarCanonicalOrdering(g)planarCanonicalOrdering(g)
g |
instance of class graphNEL from Bioconductor graph class |
see http://www.boost.org/doc/libs/1_49_0/libs/graph/doc/planar_graphs.html
A vector of ordered node names
Li Long <[email protected]>
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
The Boost Graph Library: User Guide and Reference Manual; by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine; (Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0-201-72914-8
V <- LETTERS[1:6] g <- new("graphNEL", nodes=V, edgemode="undirected") g <- addEdge(V[1+0], V[1+1], g) g <- addEdge(V[1+1], V[2+1], g) g <- addEdge(V[1+2], V[3+1], g) g <- addEdge(V[1+3], V[4+1], g) g <- addEdge(V[1+4], V[5+1], g) g <- addEdge(V[1+5], V[0+1], g) g <- addEdge(V[1+0], V[2+1], g) g <- addEdge(V[1+0], V[3+1], g) g <- addEdge(V[1+0], V[4+1], g) g <- addEdge(V[1+1], V[3+1], g) g <- addEdge(V[1+1], V[4+1], g) g <- addEdge(V[1+1], V[5+1], g) x2 <- planarCanonicalOrdering(g) x2V <- LETTERS[1:6] g <- new("graphNEL", nodes=V, edgemode="undirected") g <- addEdge(V[1+0], V[1+1], g) g <- addEdge(V[1+1], V[2+1], g) g <- addEdge(V[1+2], V[3+1], g) g <- addEdge(V[1+3], V[4+1], g) g <- addEdge(V[1+4], V[5+1], g) g <- addEdge(V[1+5], V[0+1], g) g <- addEdge(V[1+0], V[2+1], g) g <- addEdge(V[1+0], V[3+1], g) g <- addEdge(V[1+0], V[4+1], g) g <- addEdge(V[1+1], V[3+1], g) g <- addEdge(V[1+1], V[4+1], g) g <- addEdge(V[1+1], V[5+1], g) x2 <- planarCanonicalOrdering(g) x2
planarFaceTraversal description
planarFaceTraversal(g)planarFaceTraversal(g)
g |
instance of class graphNEL from Bioconductor graph class |
see http://www.boost.org/doc/libs/1_49_0/libs/graph/doc/planar_graphs.html
A list of character vectors with ordered sequences of node names
Li Long <[email protected]>
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
The Boost Graph Library: User Guide and Reference Manual; by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine; (Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0-201-72914-8
V <- LETTERS[1:9] g <- new("graphNEL", nodes=V, edgemode="undirected") g <- addEdge(V[1+0],V[1+1],g) g <- addEdge(V[1+1],V[1+2],g) g <- addEdge(V[1+3],V[1+4],g) g <- addEdge(V[1+4],V[1+5],g) g <- addEdge(V[1+6],V[1+7],g) g <- addEdge(V[1+7],V[1+8],g) g <- addEdge(V[1+0],V[1+3],g) g <- addEdge(V[1+3],V[1+6],g) g <- addEdge(V[1+1],V[1+4],g) g <- addEdge(V[1+4],V[1+7],g) g <- addEdge(V[1+2],V[1+5],g) g <- addEdge(V[1+5],V[1+8],g) x1 <- planarFaceTraversal(g) x1V <- LETTERS[1:9] g <- new("graphNEL", nodes=V, edgemode="undirected") g <- addEdge(V[1+0],V[1+1],g) g <- addEdge(V[1+1],V[1+2],g) g <- addEdge(V[1+3],V[1+4],g) g <- addEdge(V[1+4],V[1+5],g) g <- addEdge(V[1+6],V[1+7],g) g <- addEdge(V[1+7],V[1+8],g) g <- addEdge(V[1+0],V[1+3],g) g <- addEdge(V[1+3],V[1+6],g) g <- addEdge(V[1+1],V[1+4],g) g <- addEdge(V[1+4],V[1+7],g) g <- addEdge(V[1+2],V[1+5],g) g <- addEdge(V[1+5],V[1+8],g) x1 <- planarFaceTraversal(g) x1
The functions or variables listed here are no longer part of the RBGL package.
prim.minST()prim.minST()
none
The RBGL package consists of a number of interfaces to the Boost C++ library for graph algorithms. This page follows, approximately, the chapter structure of the monograph on the Boost Graph Library by Siek et al., and gives hyperlinks to documentation on R functions currently available, along with the names of formal parameters to these functions.
| Functions | parameters |
bandwidth |
g
|
bfs |
object,node,checkConn
|
dfs |
object,node,checkConn
|
edgeConnectivity |
g
|
gprofile |
g
|
isomorphism |
g1,g2
|
minCut |
g
|
transitive.closure |
g
|
tsort |
x
|
| Functions | parameters |
bellman.ford.sp |
g,start
|
dag.sp |
g,start
|
dijkstra.sp |
g,start
|
extractPath |
s,f,pens
|
johnson.all.pairs.sp |
g
|
sp.between |
g,start,finish
|
sp.between.old |
g,start,finish
|
sp.between.scalar |
g,start,finish
|
| Functions | parameters |
mstree.kruskal |
x
|
| Functions | parameters |
connectedComp |
g
|
highlyConnSG |
g,sat,ldv
|
incremental.components |
g
|
init.incremental.components |
g
|
same.component |
g,node1,node2
|
strongComp |
g
|
| Functions | parameters |
edmonds.karp.max.flow |
g,source,sink
|
push.relabel.max.flow |
g,source,sink
|
| Functions | parameters |
cuthill.mckee.ordering |
g
|
minDegreeOrdering |
g,delta
|
sloan.ordering |
g,w1,w2
|
| Functions | parameters |
circle.layout |
g,radius
|
kamada.kawai.spring.layout |
g,edge_or_side,es_length
|
| Functions | parameters |
betweenness.centrality.clustering |
g,threshold,normalize
|
| Functions | parameters |
brandes.betweenness.centrality |
g
|
| Functions | parameters |
aver.wavefront |
g
|
ith.wavefront |
g,start
|
maxWavefront |
g
|
rms.wavefront |
g
|
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
The Boost Graph Library: User Guide and Reference Manual; by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine; (Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0-201-72914-8
remove self loops in a graph
removeSelfLoops(g)removeSelfLoops(g)
g |
one instance of the |
If a given graph contains self-loop(s), removeSelfLoops removes them.
This is for those functions that cannot handle graphs with self-loops.
A new graph without self loops.
Li Long <[email protected]>
con <- file(system.file("XML/dijkex.gxl",package="RBGL")) g1 <- fromGXL(con) close(con) g2 <- ugraph(g1) removeSelfLoops(g2)con <- file(system.file("XML/dijkex.gxl",package="RBGL")) g1 <- fromGXL(con) close(con) g2 <- ugraph(g1) removeSelfLoops(g2)
The function tests to see whether a set of nodes, S1, separates
all nodes in a from all nodes in b.
separates(a, b, S1, g)separates(a, b, S1, g)
a |
The names of the nodes in the from set. |
b |
The names of the nodes in the to set. |
S1 |
The names of the nodes in the separation set. |
g |
An instance of the |
The algorithm is quite simple. A subgraph is created by removing the
nodes named in S1 from g. Then all paths between
elements of a to elements of b are tested for. If any
path exists the function returns FALSE, otherwise it returns
TRUE.
Either TRUE or FALSE depending on whether S1 separates
a from b in g1.
R. Gentleman
S. Lauritzen, Graphical Models, OUP.
con <- file(system.file("XML/kmstEx.gxl",package="RBGL")) km <- fromGXL(con) close(con) separates("B", "A", "E", km) separates("B", "A", "C", km)con <- file(system.file("XML/kmstEx.gxl",package="RBGL")) km <- fromGXL(con) close(con) separates("B", "A", "E", km) separates("B", "A", "C", km)
Compute vertex coloring for a graph
sequential.vertex.coloring(g)sequential.vertex.coloring(g)
g |
an instance of the |
A vertex coloring for a graph is to assign a color for each vertex so that no two adjacent vertices are of the same color. We designate the colors as sequential integers: 1, 2, ....
For ordered vertices, v1, v2, ..., vn, for k = 1, 2, ...,
n, this algorithm assigns vk to the smallest possible color. It does
NOT guarantee to use minimum number of colors.
See documentations on these algorithms in Boost Graph Library for more details.
no. of colors needed |
how many colors to use to color the graph |
colors of nodes |
color label for each vertex |
Li Long <[email protected]>
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
The Boost Graph Library: User Guide and Reference Manual; by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine; (Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0-201-72914-8
con <- file(system.file("XML/dijkex.gxl",package="RBGL"), open="r") coex <- fromGXL(con) close(con) sequential.vertex.coloring(coex)con <- file(system.file("XML/dijkex.gxl",package="RBGL"), open="r") coex <- fromGXL(con) close(con) sequential.vertex.coloring(coex)
sloanStartEndVertices description
sloanStartEndVertices(g)sloanStartEndVertices(g)
g |
instance of class graphNEL from Bioconductor graph class |
not used
message
Li Long <[email protected]>
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
The Boost Graph Library: User Guide and Reference Manual; by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine; (Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0-201-72914-8
dijkstra's shortest paths
sp.between(g,start,finish, detail=TRUE)sp.between(g,start,finish, detail=TRUE)
g |
instance of class graph |
start |
node name(s) for start of path(s) |
finish |
node name(s) for end of path(s) |
detail |
if TRUE, output additional info on the shortest path |
These functions are interfaces to the Boost graph library C++ routines for Dijkstra's shortest paths.
Function sp.between.scalar is obsolete.
When start and/or finish are vectors, we use the normal cycling
rule in R to match both vectors and try to find the shortest path for each
pair.
Function sp.between returns a list of info on the shortest paths. Each
such shortest path is designated by its starting node and its ending node.
Each element in the returned list contains:
length |
total length (using edge weights) of this shortest path |
,
path_detail |
if requested, a vector of names of the nodes on the shortest path |
,
length_detail |
if requested, a list of edge weights of this shortest path |
.
See pathWeights for caveats about undirected graph representation.
VJ Carey <[email protected]>, Li Long <[email protected]>
bellman.ford.sp, dag.sp, dijkstra.sp, johnson.all.pairs.sp
con <- file(system.file("XML/ospf.gxl",package="RBGL"), open="r") ospf <- fromGXL(con) close(con) dijkstra.sp(ospf,nodes(ospf)[6]) sp.between(ospf, "RT6", "RT1") sp.between(ospf, c("RT6", "RT2"), "RT1", detail=FALSE) sp.between(ospf, c("RT6", "RT2"), c("RT1","RT5")) # see NAs for query on nonexistent path sp.between(ospf,"N10", "N13")con <- file(system.file("XML/ospf.gxl",package="RBGL"), open="r") ospf <- fromGXL(con) close(con) dijkstra.sp(ospf,nodes(ospf)[6]) sp.between(ospf, "RT6", "RT1") sp.between(ospf, c("RT6", "RT2"), "RT1", detail=FALSE) sp.between(ospf, c("RT6", "RT2"), c("RT1","RT5")) # see NAs for query on nonexistent path sp.between(ospf,"N10", "N13")
The strongly connected components in a directed graph are identified and returned as a list.
strongComp(g)strongComp(g)
g |
graph with |
Tarjan's algorithm is used to determine all strongly connected components of a directed graph.
A list whose length is the number of strongly connected components in
g. Each element of the list is a vector of the node labels for
the nodes in that component.
Vince Carey <[email protected]>
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
The Boost Graph Library: User Guide and Reference Manual; by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine; (Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0-201-72914-8
connComp,connectedComp, same.component
con <- file(system.file("XML/kmstEx.gxl",package="RBGL"), open="r") km <- fromGXL(con) close(con) km<- graph::addNode(c("F","G","H"), km) km<- addEdge("G", "H", km, 1) km<- addEdge("H", "G", km, 1) strongComp(km) connectedComp(ugraph(km))con <- file(system.file("XML/kmstEx.gxl",package="RBGL"), open="r") km <- fromGXL(con) close(con) km<- graph::addNode(c("F","G","H"), km) km<- addEdge("G", "H", km, 1) km<- addEdge("H", "G", km, 1) strongComp(km) connectedComp(ugraph(km))
Compute transitive closure of a directed graph
transitive.closure(g)transitive.closure(g)
g |
an instance of the |
This function calculates the transitive closure of a directed graph. See documentation on this function in Boost Graph Library for more details.
An object of class graphNEL.
Li Long <[email protected]>
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
The Boost Graph Library: User Guide and Reference Manual; by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine; (Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0-201-72914-8
con <- file(system.file("XML/dijkex.gxl",package="RBGL")) coex <- fromGXL(con) close(con) transitive.closure(coex)con <- file(system.file("XML/dijkex.gxl",package="RBGL")) coex <- fromGXL(con) close(con) transitive.closure(coex)
Calculate transitivity for an undirected graph
transitivity(g)transitivity(g)
g |
an instance of the |
For an undirected graph G, let delta(v) be the number of triangles with
v
as a node, let tau(v) be the number of triples, i.e., paths of length 2 with
v as the center node.
Define transitivity T(G) = sum(delta(v)) / sum(tau(v)), for all v in V.
Transitivity for graph g.
Li Long [email protected]
Approximating Clustering Coefficient and Transitivity, T. Schank, D. Wagner, Journal of Graph Algorithms and Applications, Vol. 9, No. 2 (2005).
clusteringCoef, clusteringCoefAppr, graphGenerator
con <- file(system.file("XML/conn.gxl",package="RBGL")) g <- fromGXL(con) close(con) tc <- transitivity(g)con <- file(system.file("XML/conn.gxl",package="RBGL")) g <- fromGXL(con) close(con) tc <- transitivity(g)
returns vector of zero-based indices of vertices of a DAG in topological sort order
tsort(x) # now x assumed to be Bioconductor graph graphNELtsort(x) # now x assumed to be Bioconductor graph graphNEL
x |
instance of class graphNEL from Bioconductor graph class |
calls to the topological\_sort algorithm of BGL. will check in BGL whether the input is a DAG and return a vector of zeroes (of length length(nodes(x))) if it is not. Thus this function can be used to check for cycles in a digraph.
A character vector of vertices in the topological sort sequence.
VJ Carey <[email protected]>
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
The Boost Graph Library: User Guide and Reference Manual; by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine; (Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0-201-72914-8
data(FileDep) tsind <- tsort(FileDep) tsind FD2 <- FileDep # now introduce a cycle FD2 <- addEdge("bar_o", "dax_h", FD2, 1) tsort(FD2)data(FileDep) tsind <- tsort(FileDep) tsind FD2 <- FileDep # now introduce a cycle FD2 <- addEdge("bar_o", "dax_h", FD2, 1) tsort(FD2)
Compute the i-th/max/average/rms wavefront for a graph
ith.wavefront(g, start) maxWavefront(g) aver.wavefront(g) rms.wavefront(g)ith.wavefront(g, start) maxWavefront(g) aver.wavefront(g) rms.wavefront(g)
start |
a vertex of the |
g |
an instance of the |
Assorted functions on wavefront of a graph.
ith.wavefront |
wavefront of the given vertex |
maxWavefront |
maximum wavefront of a graph |
aver.wavefront |
average wavefront of a graph |
rms.wavefront |
root mean square of all wavefronts |
Li Long <[email protected]>
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
The Boost Graph Library: User Guide and Reference Manual; by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine; (Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0-201-72914-8
con <- file(system.file("XML/dijkex.gxl",package="RBGL"), open="r") coex <- fromGXL(con) close(con) ss <- 1 ith.wavefront(coex, ss) maxWavefront(coex) aver.wavefront(coex) rms.wavefront(coex)con <- file(system.file("XML/dijkex.gxl",package="RBGL"), open="r") coex <- fromGXL(con) close(con) ss <- 1 ith.wavefront(coex, ss) maxWavefront(coex) aver.wavefront(coex) rms.wavefront(coex)