Skip to content

News

SocNetV 2.3 Released

The Social Network Visualizer project is happy to announce that a brand new version of our favorite social network analysis and visualization software application has been released. SocNetV version 2.3, released on Jul 5, has the eloquent codename “fixer” and it is now available for Windows, Mac OS X, and Linux from the Downloads page.

What the new version brings to the users?

As usual with our odd-numbered minor versions, this is a bugfix release. While v2.2 brought a lot of new and important features, v2.3 focuses on stability and fixing bugs here and there. Nevertheless, there are a couple of new features as well, which you may find interesting for your network analysis endeavors:

Dyad and Actor/Ego Reciprocity

Reciprocity, denoted by ‘r,’ is a network cohesion index. It measures the likelihood of vertices in a directed network to be mutually linked. SocNetV v2.3 supports two different methods to index the degree of reciprocity in a social network:

  • Arc reciprocity: The fraction of reciprocated ties over all actual ties in the network.
  • Dyad reciprocity: The fraction of actor pairs that have reciprocated ties over all pairs of actors that have any connection.

In a directed network:

  • Arc reciprocity measures the proportion of directed edges that are bidirectional. If the reciprocity is 1, the adjacency matrix is structurally symmetric.
  • Dyad reciprocity measures the proportion of connected actor dyads that have bidirectional ties between them.

In an undirected graph, all edges are reciprocal, and the reciprocity of the graph is always 1.

You can compute reciprocity on undirected, directed, and weighted graphs from the toolbox/menu: Analyze > Cohesion > Reciprocity. The report is opened and displayed as usual in HTML format in your preferred web browser.

Zero-weighted Edge Support

In ordinary SNA, zero-weighted edges are thought to be meaningless, but a user pointed out that SocNetV was accepting and drawing zero-valued edges when opening edge list formatted files. This behavior was due to a bug.

The concept of an edge in SNA (and SocNetV) is that two actors (i) and (j) are “connected” (and an edge is drawn between them) only if there is a non-zero value at sociomatrix cell (A(i,j)). Thus, if (A(i,j) = 0), the actors are considered not directly connected, and no edge should be drawn between them.

This assumption has implications for computations: all centrality and matrix manipulation algorithms of network analysis implicitly compute their results using only non-zero (and some only positive) weighted edges.

To accommodate users who may need this functionality for visualization purposes, we implemented support for zero-weighted edges in v2.3. This functionality currently works only for weighted edge lists and for simple visualizations.

Zero-weighted Edge Color Selection

With the new feature above, the Settings dialog allows users to select a default edge color for zero-valued edges. Here is a screenshot:

SocNetV v2.3 Settings Dialog

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

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!

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

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.