Is this object a graph?
Rewires the endpoints of the edges of a graph randomly
Common functions supporting community detection algorithms
Decompose a graph into components
Closeness centrality of vertices
Edge connectivity.
Vertex and edge betweenness centrality
Vertex and edge sequences and iterators
Minimum spanning tree
Graph Isomorphism
Generate random graphs with a given degree sequence
Merging graph layouts
Modularity of a community structure of a graph
Convert a graph to an adjacency matrix or an edge list
Graph density
Shortest (directed or undirected) paths between vertices
K-core decomposition of graphs
Fitting a power-law distribution function to discrete data
Growing random graph generation
Method for structural manipulation of graphs
Undocumented and unsupportted igraph functions
Graph Laplacian
Print graphs to the terminal
Interactive plotting of graphs
Topological sorting of vertices in a graph
Diameter of a graph
Neighborhood of graph vertices
Convert between directed and undirected graphs
Independent vertex sets
Graph, vertex and edge attributes
Graph motifs
The functions find cliques, ie. complete subgraphs in a graph
Generate coordinates for plotting graphs
Graph rewiring
Load a graph from the graph database for testing graph
isomorphism.
Community strucure via short random walks
leading.eigenvector.community
Community structure detecting based on the leading eigenvector
of the community matrix
The Page Rank algorithm
Find Bonacich alpha centrality scores of network positions
Community structure via greedy optimization of modularity
Graph operators
Geometric random graphs
Generate an evolving random graph with preferential attachment
and aging
In- or out- component of a vertex
Trait-based random generation
Running mean of a time series
Girth of a graph
Remove loop and/or multiple edges from a graph
Reciprocity of graphs
Parameters for the igraph package
Finding communities in graphs based on statistical meachanics
edge.betweenness.community
Community structure detection based on edge betweenness
Measuring the driving force in evolving networks
Subgraph of a graph
Connected components of a graph
Drawing graphs
Cocitation coupling
Measuring the driving force in evolving networks
Generate random graphs according to the Erdos-Renyi model
Writing the graph to a file in some format
Plotting of graphs
Burt's constraint
Graph generation based on different vertex types
Find Eigenvector Centrality Scores of Network Positions
Gaining information about graph structure
Transitivity of a graph
Reading foreign file formats
The Watts-Strogatz small-world model
Various methods for creating graphs
Generate scale-free graphs according to the Barabasi-Albert
model
Find Bonacich Power Centrality Scores of Network Positions
Degree and degree distribution of the vertices
Sampling a random integer sequence
3D plotting of graphs with OpenGL
Maximum flow in a network
Vertex connectivity.