SNAP Package Description

• SNAP Directory Structure
• Example Applications
• Functionality of the Core SNAP Library

SNAP Directory Structure

The SNAP package contains the following directories:

• snap-core:the core SNAP graph library;
• snap-adv:advanced SNAP components, not in the core, but used by examples;
• snap-exp:experimental SNAP components, still in development;
• examples:small sample applications that demonstrate SNAP functionality;
• tutorials:simple programs, demonstrating use of various classes;
• glib-core: STL-like library that implements basic data structures, like vectors (TVec), hash-tables (THash) and strings (TStr), provides serialization and so on;
• test:unit tests for various classes;
• doxygen:SNAP reference manuals.

Example Applications

• 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.
• 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.
• cliques: Finds overlapping dense groups of nodes in networks, based on the Clique Percolation Method.
• community: Implements network community detection algorithms: Girvan-Newman and Clauset-Newman-Moore.
• concomp: Computes weakly, strongly and biconnected connected components, articulation points and bridge edges of a graph.
• 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.
• magfit: Estimates Multiplicative Attribute Graph (MAG) model parameter.
• maggen: Generates Multiplicative Attribute Graphs (MAG).
• mkdatasets: Demonstrates how to load different kinds of networks in various network formats and how to compute various statistics of the network.
• 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.
• testgraph: Demonstrates some of the basic SNAP functionality.

Functionality of the Core SNAP Library

Brief description of SNAP functionality implemented in various files in the distribution package: