Social Network Visualizer 3.2
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Author:
Social Network Visualizer
Date: 04/02/2025 Size: 30-45 MB License: Open Source Requires: 11|10|8|7|Linux|macOS Downloads: 5779 times 
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Social Network Visualizer (SocNetV) is a social network analysis and visualization application.
Social Network Visualizer can draw a social network (graph/digraph) or load an existing one (GraphML, UCINET, Pajek, etc), compute cohesion, centrality, community and structural equivalence metrics and apply various layout algorithms based on actor centrality or prestige scores (i.e. Eigenvector, Betweenness) or on dynamic models (i.e. spring-embedder)
Social Network Visualizer Features:
Auto create known network data sets
Random network models (scale-free, Erdős–Rényi G(n,p) and G(n, M), Watts-Strogatz small worlds, ring lattice, d-regular, etc.)
Supports most social network formats (GraphML, Adjacency, Pajek, UCINET, lists, etc.)
Works with two-mode affiliation networks (in sociomatrix format)
Fast calculation of Centrality indices (Degree, Closeness, Betweenness, Graph, Information, Power) as well as Prestige (Proximity, Rank, etc.)
Radial network layouts according to centrality and prestige indices
PageRank Analysis
Leveled network layout according to prominence indices
Crawl websites to create networks
Triad and Clique Census
Structural equivalence methods: Hierarchical cluster analysis, Actor Similarity, Tie profile dissimilarities, Pearson coefficients
Exports in GraphML, Pajek, Adjacency
Online manual and technical documentation available
Reads and writes networks with multiple relations, enables you to edit links from different relations and perform analysis
Fast enough for large networks of >1000 actors and 10000 edges.
Low memory and CPU use
Graph and digraph mode
Social Network Visualizer can draw a social network (graph/digraph) or load an existing one (GraphML, UCINET, Pajek, etc), compute cohesion, centrality, community and structural equivalence metrics and apply various layout algorithms based on actor centrality or prestige scores (i.e. Eigenvector, Betweenness) or on dynamic models (i.e. spring-embedder)
Social Network Visualizer Features:
Screenshot for Social Network Visualizer
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