GraphAnalysis

Graph Analysis

Graph Analysis can be a vague term (used in a wide or narrow sense). Here we consider "analysis" to mean rather direct and/or transparent processing that does not involve elaborate graph algorithms - the focus is on the measures produced more than documenting how they are produced.

Graph Classification / Characterization

Bipartite
Directed Acyclic Graph (DAG)
Planar Graph
Graph Characterisation
Trees
Simple
Directed
Connected

Graph Transformations

Manipulating Graphs

Access, Add, Remove Vertices and edges

Permutations

Topological Sorting

a vertex property map the dominator vertices for each vertex the topological sort of the given graph transitive closure graph of g

Return the line graph of the given graph g.

Obtain the condensation graph, where each vertex with the same 'prop' value is condensed in one vertex.

Remove all self-loops edges from the graph.

Graph Operations

union, intersection, difference, and other set operations (operators) between graphs

Graph Analysis / Network Analysis

Find Incidence Find Neighborhood Compute Node Degrees

Global Graph Properties

Diameter
Girth
Radius

Distributions / Moments

Degree Distribution

vertex histogram of the given degree type or property edge histogram of the given property average of the given degree or vertex property average of the given degree or vertex property shortest-distance histogram for each vertex pair in the graph

Topology

Path Length
Shortest Distance
Shortest Path
Random Shortest Path
All Circuits
Pseudo-Diameter

Connectedness

Centrality

PageRank
Betweeness
Central Point Dominance
Closeness
Eigevector Centrality
Katz
hits
EigenTrust
Pervasive Trust Transitivity

Similarity

Clustering

Local Clustering Coefficients
Global Clustering Coefficients
Extended Clustering
Motifs
Motif Significance

Correlations

Assortativity
Scalar Assortativity
Correlation Histogram
Single-Vertex Combined Correlation Histogram

Cliques and Motifs

This includes the computation of various Statistics and Metrics