On the visualization of the detected communities in dynamic networks: A case study of Twitter's network

TL;DR

This paper presents a novel approach for analyzing and visualizing communities in dynamic Twitter networks, combining a community detection algorithm with a visualization tool to better understand evolving social interactions.

Contribution

It introduces Dyci, a dynamic community detection algorithm, and NLCOMS, a visualization tool, specifically designed for analyzing changing social networks like Twitter.

Findings

Effective detection of community changes over time.

Successful visualization of dynamic social interactions.

Application to real Twitter data demonstrates practical utility.

Abstract

Understanding the information behind social relationships represented by a network is very challenging, especially, when the social interactions change over time inducing updates on the network topology. In this context, this paper proposes an approach for analysing dynamic social networks, more precisely for Twitter's network. Our approach relies on two complementary steps: (i) an online community identification based on a dynamic community detection algorithm called Dyci. The main idea of Dyci is to track whether a connected component of the weighted graph becomes weak over time, in order to merge it with the "dominant" neighbour community. Additionally, (ii) a community visualization is provided by our visualization tool called NLCOMS, which combines between two methods of dynamic network visualization. In order to assess the efficiency and the applicability of the proposed approach, we consider real-world data of the ANR-Info-RSN project, which deals with community analysis in Twitter.