A Real-Time Detecting Algorithm for Tracking Community Structure of Dynamic Networks

TL;DR

This paper introduces a real-time, low-complexity algorithm for tracking community structures in dynamic networks, effectively updating communities over time with high modularity.

Contribution

It presents an incremental algorithm that combines static community detection with dynamic updating strategies, enabling efficient real-time tracking of evolving network communities.

Findings

Outperforms CNM algorithm in modularity on real datasets

Maintains high modularity with low computational complexity

Successfully tracks community evolution in dynamic networks

Abstract

In this paper a simple but efficient real-time detecting algorithm is proposed for tracking community structure of dynamic networks. Community structure is intuitively characterized as divisions of network nodes into subgroups, within which nodes are densely connected while between which they are sparsely connected. To evaluate the quality of community structure of a network, a metric called modularity is proposed and many algorithms are developed on optimizing it. However, most of the modularity based algorithms deal with static networks and cannot be performed frequently, due to their high computing complexity. In order to track the community structure of dynamic networks in a fine-grained way, we propose a modularity based algorithm that is incremental and has very low computing complexity. In our algorithm we adopt a two-step approach. Firstly we apply the algorithm of Blondel et al for detecting static communities to obtain an initial community structure. Then, apply our incremental updating strategies to track the dynamic communities. The performance of our algorithm is measured in terms of the modularity. We test the algorithm on tracking community structure of Enron Email and three other real world datasets. The experimental results show that our algorithm can keep track of community structure in time and outperform the well known CNM algorithm in terms of modularity.