Table IV.

Graph manipulation and analytics methods in SNAP.

Category	Graph Manipulation and Analytics
Graph manipulation	Graph rewiring, decomposition to connected components, subgraph extraction, graph type conversions;
Connected components	Analyze weakly, strongly, bi- and 1-connected components;
Node connectivity	Node degrees, degree distribution, in-degree, out-degree, combined degree, Hop plot, Scree plot;
Node centrality algorithms	PageRank, Hits, degree-, betweenness-, closeness-, farness-, and eigen-centrality, personalized PageRank;
Triadic closure algorithms	Node clustering coefficient, triangle counting, clique detection;
Graph traversal	Breadth first search, depth first search, shortest paths, graph diameter;
Community detection	Fast modularity, clique percolation, link clustering, Community-Affiliation Graph Model, BigClam, CoDA, CESNA, Circles;
Spectral graph properties	Eigenvectors and eigenvalues of the adjacency matrix, spectral clustering;
K-core analysis	Identification and decomposition of a given graph to k-cores;
Graph motif detection	Counting of small subgraphs;
Information diffusion	Infopath, Netinf;
Network link and node prediction	Predicting missing nodes, edges and attributes.