Social networks represent relationships involving social entities such as friendships among individuals, communication in a group, or transactions between corporations. Finding important actors, discovering cohesive groups or communities, or identifying actors that are similar in some way are all examples of analysis that can be done for social networks.  Building on its strong graph capabilities, the Wolfram Language allows you to model and analyze networks in a flexible and powerful way. Social networks are accessible from a variety of sources, including directly from social media (Facebook, Twitter, …). High-level functions make it easy to detect communities, find cohesive groups, and visualize the results. A full suite of social network measures makes it possible to explore networks, rank actors from their centralities, or provide recommendations based on similar actors.

Social Network Data and Representation »

Graph — represent a graph with actors and links

Network Visualization

CommunityGraphPlot — visualize communities in graphs

HighlightGraph  ▪  GraphPlot  ▪  LayeredGraphPlot  ▪  TreePlot

Cohesive Groups

FindClique — find cliques

FindGraphCommunities — find communities

FindKClique  ▪  FindKClan  ▪  FindKClub  ▪  FindKPlex  ▪  KCoreComponents  ▪  ConnectedComponents  ▪  LambdaComponents  ▪  LuccioSamiComponents

Centrality and Prestige

DegreeCentrality — number of direct links to other actors

ClosenessCentrality — inverse average distance to every other actor

BetweennessCentrality — fraction of shortest paths that pass through the actor

RadialityCentrality  ▪  EccentricityCentrality  ▪  PageRankCentrality  ▪  LinkRankCentrality  ▪  KatzCentrality  ▪  HITSCentrality  ▪  StatusCentrality  ▪  EdgeBetweennessCentrality  ▪  EigenvectorCentrality

Reciprocity and Transitivity

GraphReciprocity — fraction of directed links that are reciprocated

GlobalClusteringCoefficient — fraction of length-two paths that are closed

MeanClusteringCoefficient  ▪  LocalClusteringCoefficient

Homophily, Assortative Mixing, and Similarity

GraphAssortativity — within-groups connectivity minus between-group connectivity

VertexCorrelationSimilarity — correlation similarity between actors

MeanNeighborDegree  ▪  MeanDegreeConnectivity  ▪  VertexDiceSimilarity  ▪  VertexJaccardSimilarity  ▪  VertexCosineSimilarity

Statistical Analysis »

RandomGraph — construct random graphs from symbolic graph distributions

GraphPropertyDistribution — distribution of a graph property

BarabasiAlbertGraphDistribution  ▪  WattsStrogatzGraphDistribution  ▪  BernoulliGraphDistribution  ▪  NProbability  ▪  NExpectation  ▪  ...