Snap

SNAP

• Main page of SNAP
• Ringo: In-Memory Graph Exploration System
  • Ringo: Interactive Graph Analytics on Big-Memory Machines
• SNAP Tutorial at www'15

Algorithm implemented in Snap

• 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.
• kronfit: Estimates Kronecker graph parameter matrix.
• krongen: Generates Kronecker graphs.
• lshtest: Implements locality sensitive hashing.
• magfit: Estimates Multiplicative Attribute Graph (MAG) model parameter.
• maggen: Generates Multiplicative Attribute Graphs (MAG).
• 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.