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Social Network Analysis

3 posts with the tag “Social Network Analysis”

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 as "Intelligence" Source for Dolphin-Site Operators

Dimitris

Can you use SocNetV to analyze a virtual “social network”? Yes, you can! For instance, you could analyze social interactions (e.g., mentions) between “friends” in a virtual network. Although SocNetV cannot directly grab data from private networks (its built-in web crawler is limited to publicly accessible data), if you extract the network data yourself and transform it into GraphML format, SocNetV can load and visualize the virtual network for analysis.

This approach is particularly useful for platforms like Dolphin, an open-source platform for social networks, and its mobile-friendly sibling Trident. These platforms support social connections, conversations, locations, and other social graph-related data types, which can be analyzed using SocNetV based on operator needs.

Example Use Case

Imagine an airline company launches a social network for its clients. By using SocNetV to analyze connections, discussion trends, and user posts, the company could:

  • Identify which routes are in demand.
  • Plan and optimize scheduling based on social data.
  • Target specific user segments with special offers.
  • Advertise these offers directly within the social network.

This innovative application of SocNetV showcases its potential as an “intelligence” source for network operators, enabling data-driven decision-making and enhanced user engagement.

Kudos to the Dolphin and Trident teams for exploring this exciting use case for SocNetV!