agmfit: Detects network communities from a given network by fitting the Affiliation Graph Model (AGM) to the given network by maximum likelihood estimation.
agmgen: Implements the Affiliation Graph Model (AGM). AGM generates a realistic looking graph from the community affiliation of the nodes.
bigclam: Formulates community detection problems into non-negative matrix factorization and discovers community membership factors of nodes.
cascadegen: Identifies cascades in a list of events.
cascades: Simulates a SI (susceptible-infected) model on a network and computes structural properties of cascades.
centrality: Computes node centrality measures for a graph: closeness, eigen, degree, betweenness, page rank, hubs and authorities.
cesna: Implements a large scale overlapping community detection method for networks with node attributes based on Communities from Edge Structure and Node Attributes (CESNA).
circles: Implements a method for identifying users social circles.
cliques: Finds overlapping dense groups of nodes in networks, based on the Clique Percolation Method.
coda: Implements a large scale overlapping community detection method based on Communities through Directed Affiliations (CoDA), which handles directed as well as undirected networks. The method is able to find 2-mode communities where the member nodes form a bipartite connectivity structure.
community: Implements network community detection algorithms: Girvan-Newman, Clauset-Newman-Moore and Infomap.
concomp: Computes weakly, strongly and biconnected connected components, articulation points and bridge edges of a graph.
flows: Computes the maximum network flow in a network.
forestfire: Generates graphs using the Forest Fire model.
graphgen: Generates undirected graphs using one of the many SNAP graph generators.
graphhash: Demonstrates the use of TGHash graph hash table, useful for counting frequencies of small subgraphs or information cascades.
infopath: Implements stochastic algorithm for dynamic network inference from cascade data, see Structure and Dynamics of Information Pathways in On-Line Media
kcores: Computes the k-core decomposition of the network and plots the number of nodes in a k-core of a graph as a function of k.
kronem: Estimates Kronecker graph parameter matrix using EM algorithm.
motifcluster: Implements a spectral method for motif-based clustering.
motifs: Counts the number of occurence of every possible subgraph on K nodes in the network.
ncpplot: Plots the Network Community Profile (NCP).
netevol: Computes properties of an evolving network, like evolution of diameter, densification power law, degree distribution, etc.
netinf: Implements netinf algorithm for network inference from cascade data, see Inferring Networks of Diffusion and Influence
netstat: Computes structural properties of a static network, like degree distribution, hop plot, clustering coefficient, distribution of sizes of connected components, spectral properties of graph adjacency matrix, etc.
randwalk: Computes Personalized PageRank between pairs of nodes.
rolx: Implements the rolx algorithm for analysing the structural roles in the graph.
testgraph: Demonstrates some of the basic SNAP functionality.