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Stanford Network Analysis Platform (SNAP) is a general purpose network analysis and graph mining library.
C++ C Other


  SNAP : Stanford Network Analysis Platform

Stanford Network Analysis Platform (SNAP) is a general purpose, high
performance system for analysis and manipulation of large networks.
SNAP is written in C++ and it scales to massive graphs with hundreds
of millions of nodes and billions of edges.


Directory structure:

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

Code compiles under Windows (Microsoft Visual Studio, CygWin with gcc) and
Linux and Mac (gcc). Use the SnapExamples.vcproj or provided makefiles.

Some of applications expect that GnuPlot and GraphViz are installed and
accessible -- paths are in the system PATH variable or they reside in the
working directory.


Example applications for advanced SNAP functionality are available
in the examples directory and described at:

To compile from the command line, execute:
  make all   # compiles SNAP and all sample applications

To compile on Mac OSX, using Xcode:
  1. From the Toolbar, select Scheme (e.g. 'bigclam').
  2. Product -> Build.  (or Cmd + B).
  3. Run executable via the command line; or
     Choose the scheme's executable (Product -> Edit Scheme -> Run -> Info)
     and run: Product -> Run (or Cmd + R). 
     Note: If using Gnuplot, add the PATH to the scheme's environment variables.
     or create symlink to /usr/bin:
     sudo ln -s <gnuplot_dir>/gnuplot /usr/bin/
  For code completion, the "docs" target has been created which includes all
  Snap-related files and example programs.

Description of examples:
  agmfit :
        Detects network communities from a given network by fitting
	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.
  cesna :
        Implements a large scale overlapping community detection method
        for networks with node attributes based on Communities from
        Edge Structure and Node Attributes (CESNA).
  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
  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 occurrence of every possible subgraph on K nodes 
  	in the network.
  motifcluster : 
  	Implements a spectral method for motif-based clustering.	
  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
  netstat :
  	Computes statistical 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.
  rolx :
        Implements the rolx algorithm for analysing the structural
        roles in the graph.
  testgraph :
	Demonstrates some of the basic SNAP functionality.
  zygote :
        Demonstrates how to use SNAP with the Zygote library, which
        significantly speeds up computations that need to process the
        same large graph many times.


SNAP documentation:

The library defines Graphs (nodes and edges) and Networks (graphs with data
associated with nodes and edges).

Graph types:
  TNGraph : 
  	directed graph (single directed edge between a pair of nodes)
  TUNGraph : 
  	undirected graph (single undirected edge between a pair of nodes)
  TNEGraph : 
  	directed multi-graph (multiple directed edges can exist between
        a pair of nodes)

Network types:
  TNodeNet<TNodeData> : 
  	like TNGraph, but with TNodeData object for each node
  TNodeEDatNet<TNodeData,TEdgeData> :
        like TNGraph, but with TNodeData object for each node and TEdgeData
        object for each edge
  TNodeEdgeNet<TNodeData, TEdgeData> : 
  	like TNEGraph but with TNodeData object for each node and TEdgeData
	object for each edge
  TNEANet :
        like TNEGraph, but with attributes on nodes and edges. The attributes
        are dynamic in that they can be defined at runtime
  TBigNet<TNodeData> : 
  	memory efficient implementation of TNodeNet (avoids memory

To generate reference manuals, install doxygen (, and execute:
  cd doxygen; make all    # generates user and developer reference manuals


SNAP tutorials

Sample programs demonstrating the use of foundational SNAP classes and
functionality are available in the tutorials directory.

To compile all the tutorials, execute the following command line:
  cd tutorials; make all    # generates all the tutorials


SNAP unit tests

Unit tests are available in the test directory.

To run unit tests, install googletest ( and
  cd test; make run    # compiles and runs all the tests


Description of SNAP files:

  alg.h : Simple algorithms like counting node degrees, simple graph
        manipulation (adding/deleting self edges, deleting isolated nodes)
        and testing whether graph is a tree or a star.
  anf.h : Approximate Neighborhood Function: linear time algorithm to
        approximately calculate the diameter of massive graphs.
  bfsdfs.h : Algorithms based on Breath First Search (BFS) and Depth First
        Search (DFS): shortest paths, spanning trees, graph diameter, and
  bignet.h : Memory efficient implementation of a network with data on
        nodes. Use when working with very large networks.
  centr.h : Node centrality measures: closeness, betweenness, PageRank, ...
  cmty.h : Algorithms for network community detection: Modularity,
        Girvan-Newman, Clauset-Newman-Moore.
  cncom.h : Connected components: weakly, strongly and biconnected
        components, articular nodes and bridge edges.
  ff.h : Forest Fire model for generating networks that densify and have
        shrinking diameters.
  flow.h: Maximum flow algorithms.
  gbase.h : Defines flags that are used to identify functionality of graphs.
  ggen.h : Various graph generators: random graphs, copying model,
        preferential attachment, RMAT, configuration model, Small world model.
  ghash.h : Hash table with directed graphs (<tt>TNGraph</tt>) as keys. Uses
        efficient adaptive approximate graph isomorphism testing to scale to
        large graphs. Useful when one wants to count frequencies of various
        small subgraphs or cascades.
  gio.h : Graph input output. Methods for loading and saving various textual
        and XML based graph formats: Pajek, ORA, DynNet, GraphML (GML), 
  graph.h : Implements graph types TUNGraph, TNGraph and TNEGraph.
  gstat.h : Computes many structural properties of static and evolving networks.
  gsvd.h : Eigen and singular value decomposition of graph adjacency matrix.
  gviz.h : Interface to GraphViz for plotting small graphs.
  kcore.h : K-core decomposition of networks.
  network.h : Implements network types TNodeNet, TNodeEDatNet and TNodeEdgeNet.
  Snap.h : Main include file of the library.
  statplot.h : Plots of various structural network properties: clustering,
        degrees, diameter, spectrum, connected components.
  subgraph.h : Extracting subgraphs and converting between different
        graph/network types.
  timenet.h : Temporally evolving networks.
  triad.h : Functions for counting triads (triples of connected nodes in the
        network) and computing clustering coefficient.
  randwalk.h : Computing random walk scores and personalized PageRank between pairs of nodes
  util.h : Utilities to manipulate PDFs, CDFs and CCDFs. Quick and dirty
        string manipulation, URL and domain manipulation routines.

  agm*.h : Implements the Affiliation Graph Model (AGM).
  cliques.h : Maximal clique detection and Clique Percolation method.
  graphcounter.h : Performs fast graph isomorphism testing to count the
        frequency of topologically distinct sub-graphs.
  kronecker.h : Kronecker Graph generator and KronFit algorithm for
        estimating parameters of Kronecker graphs.
  mag.h : Implements the Multiplicative Attribute Graph (MAG).
  motifcluster.h : Implements motif-based clustering algorithms.
  ncp.h : Network community profile plot. Implements local spectral graph
        partitioning method to efficiently find communities in networks.
  rolx.h : Node role detection.
  subgraphenum.h : Enumerates all connected induced sub-graphs of particular

  arxiv.h : Functions for parsing Arxiv data and standardizing author names.
  dblp.h : Parser for XML dump of DBLP data.
  imdbnet.h : Actors-to-movies bipartite network of IMDB.
  mxdag.h  Finds the maximum directed-acyclic subgraph of a given
        directed graph.
  signnet.h : Networks with signed (+1, -1) edges that can denote
        trust/distrust between the nodes of the network.
  sir.h : SIR epidemic model and SIR parameter estimation.
  spinn3r.h : Past parser for loading blog post data from Spinn3r.
  trawling.h : Algorithm of extracting bipartite cliques from the network.
  wgtnet.h : Weighted networks.
  wikinet.h : Networks based on Wikipedia.

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