Skip to content
SocNetV SocNetV
News

News

SocNetV v2.2 Released with Cluster Analysis, Eigenvector Centrality, and More!

Dimitris

We are pleased to announce that a new version of your favorite social network analysis and visualization software application has been released. SocNetV version 2.2, codenamed “beyond”, brings many new features and is now available for Windows, Mac OS X, and Linux. Visit the Downloads page to get it!

What’s New in SocNetV v2.2?

Hierarchical Clustering Analysis (HCA)

SocNetV now performs hierarchical agglomerative cluster analysis on social networks using these methods:

  • Single-Linkage (minimum)
  • Complete-Linkage (maximum)
  • Average-Linkage (UPGMA)

You can compute the Structural Equivalence matrix using adjacency or geodesic distance matrices with user-selected metrics such as Euclidean, Manhattan, and Jaccard distances.

HCA Dialog

Results include:

  • A list of clusters per level.
  • A dendrogram of the cluster hierarchy in SVG format.

HCA Results

Eigenvector Centrality

Version 2.2 introduces Eigenvector Centrality, which measures the influence of a node in a network based on the leading eigenvector of the adjacency matrix.

Eigenvector Centrality

Use this metric for network analysis or embedding radial/level layouts.

Eigenvector Layout

Pearson Product-Moment Correlation Coefficients

SocNetV now computes Pearson Correlation Coefficients to correlate actor profiles (ties or distances). Results are displayed as a correlation matrix.

Pearson Coefficients Dialog

Actor Similarity

Compare pairwise tie/distance profiles of actors to produce a similarity matrix using measures like:

  • Simple Matching
  • Jaccard
  • Hamming
  • Cosine Similarity
  • Euclidean Distance

Maximal Clique Census

Using the Bron–Kerbosch algorithm, SocNetV finds all maximal cliques in undirected or directed graphs. The clique census report includes useful statistics, co-membership information, and dendrograms.

Clique Census Report

Additional Features

Cocitation Analysis

  • Compute Cocitation Matrices.
  • Create Cocitation Networks, where actors are connected if they are cited by common neighbors.

Symmetrize Edges by Strong Ties

Create new relations using only strong, reciprocal ties.

Multi-relational GraphML Support

Read and write .graphml files with multiple relations.

GML and Pajek Support

Support for GML formatted data and multi-relational Pajek files.

Performance Improvements

  • Faster matrix multiplication using optimized algorithms.
  • Enhanced adjacency matrix plotting with Unicode characters.

Adjacency Matrix Plot

Bug Fixes and Notices

  • Resolved various issues like incorrect distances in weighted networks, edge labels not saving, and more.
  • New dataset: Petersen Graph.
  • Transformed Krackhardt’s High-tech Managers and Zachary Karate Club into multirelational datasets.

Important Notices:

Download SocNetV v2.2 today and enjoy the new features and improvements!

SocNetV v2.1 Released!

Dimitris

Today is a wonderful day because we are happy to announce that a brand-new version of our favorite social network analysis and visualization software application has been released. SocNetV version 2.1, released on September 28, 2016, has the codename “fixer” and is available for Windows, Mac OS X, and Linux from the Downloads page.

What’s New in SocNetV v2.1?

Faster and More Accurate Network Analysis Computation

  • Improved algorithms for social network analysis allow most metrics to be computed simultaneously. The results are saved and reused during the session, recalculating only when nodes, edges, or weights are modified.
  • Fixed metrics like PageRank Prestige (PRP) and Average Graph Distance (AGD) now produce accurate results.

New d-Regular Random Network Generator

  • The d-regular network generator has been rewritten and now generates both directed and undirected d-regular random networks without errors.

Improved UCINET Format Support

  • Fullmatrix format is now supported again. SocNetV already supports the edgelist format, ensuring compatibility with more datasets.

Better Network Visualization

  • Fixed issues with node and edge stacking on the canvas.
  • Corrected the display of edges with large weights to prevent overly thick lines.

Bug Fixes

  • #1624561: Network files with both arcs and edges are loaded as solely undirected nets.
  • #1622889: The d-regular generator does not produce random networks.
  • #1623812: After loading a new network file, the app behaves as if the network has changed.
  • #1624583: UCINET .dl files crash the app.
  • #1624750: Random new nodes can be drawn out of the canvas.
  • #1625831: Removing an edge in undirected graphs does not update the node outDegree.
  • #1627390: Wrong PageRank Prestige results in undirected nets.
  • #1627721: Incorrect average graph distance metric in disconnected networks.
  • #1628382: Edges with very large weights are drawn with huge line widths.
  • #1627213: Crashes when double-clicking on a target node after deleting the source node.
  • #1628170: Edge labeling with HTML special characters breaks GraphML files.
  • #1622891: Highlighted edges should have a larger z-index.
  • #1624352: The “Change Edge Color” dialog does not display the current edge color.
  • #1624360: Default edge color and node shape are incorrect in Edit menu dialogs.
  • #1628395: Incorrect z-value of nodes and edges caused cluttering.

Download SocNetV v2.1 today and, as always, have fun with your social network analysis projects!

SocNetV v1.9 released - Bug Fixes and Speed Increase

Dimitris

The Social Network Visualizer project has just released version 1.9, which fixes many important bugs and brings a faster matrix inverse routine. Source code, Windows zipped executables, Mac OS image, and binary packages for major Linux distributions are as always available from the Downloads page.

Key Improvements

  • Faster Matrix Inverse Routine:
    The matrix inverse algorithm now uses LU decomposition, greatly improving computation speed. This enhancement also affects the Information Centrality algorithm, which now runs in 1/10th of the time required in earlier SocNetV versions.

  • Revamped PageRank Prestige Algorithm:
    Up to version 1.8, the PageRank algorithm used the original Page & Brin formula, leading to different results. Starting from this version, SocNetV uses the correct formula and computes comparable results. Additionally, the initial PageRank score of each node is now set to 1/N.

Closed Bugs in Version 1.9

  • #1463069: Wrong average distance when there are isolates.
  • #1365037: Certain sparse matrices crash SocNetV on the invertMatrix method.
  • #1365582: CentralityInformation() is slow when network size (N > 100).
  • #1463095: Edge filter works but the user cannot undo.
  • #1464422: Incorrect PageRank results.
  • #1464430: SocNetV refuses to read Pajek files not starting with *Network.
  • #1465774: Edges do not always follow relations.
  • #1463082: Edge color change does not take effect.
  • #1464418: SocNetV crashes during PageRank computation on isolated nodes.

With these enhancements and fixes, SocNetV v1.9 ensures faster and more reliable performance for your social network analysis tasks.

SocNetV v1.8 Released with Scale-Free Network Generator

Dimitris

A new version of SocNetV has just been released with a couple of nice new features.

Clique Computation Routine

SocNetV v1.8 includes a revamped clique computation routine with an updated report. The new “clique census” report provides:

  • Aggregate counts of cliques (up to clique number 4).
  • Disaggregation by vertex and co-membership information.

Clique Census Example

Scale-Free Network Generator

This version introduces the ability to generate scale-free random networks/graphs using the Barabási–Albert (BA) model, which employs a preferential attachment mechanism.

The algorithm starts with ( m_0 ) connected nodes (default: 1). At each step, a new node is added with ( m ) edges to existing nodes. The probability ( p_i ) that the n