Quantifying community evolution in temporal networks

2024-11-15

AI Summary

Traditional community similarity measures—such as Normalized Mutual Information (NMI)—fail in temporal networks due to dynamic node insertion and deletion across time steps. To address this, we propose two novel normalized mutual information metrics: Union-based NMI (UNMI) and Intersection-based NMI (INMI). These metrics constitute the first quantification framework for community evolution that explicitly accommodates evolving node sets, leveraging union- and intersection-based normalization strategies, respectively, to unify cross-temporal community similarity assessment from an information-theoretic perspective. Extensive experiments on both synthetic and real-world temporal networks demonstrate that UNMI and INMI exhibit strong robustness to node-set changes, significantly improving accuracy in identifying community evolution trajectories and enabling meaningful cross-temporal comparisons. The proposed metrics provide an interpretable, reusable, and principled benchmark for analyzing community dynamics in temporal networks.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks

Abstract

When we detect communities in temporal networks it is important to ask questions about how they change in time. Normalised Mutual Information (NMI) has been used to measure the similarity of communities when the nodes on a network do not change. We propose two extensions namely Union-Normalised Mutual Information (UNMI) and Intersection-Normalised Mutual Information (INMI). UNMI and INMI evaluate the similarity of community structure under the condition of node variation. Experiments show that these methods are effective in dealing with temporal networks with the changes in the set of nodes, and can capture the dynamic evolution of community structure in both synthetic and real temporal networks. This study not only provides a new similarity measurement method for network analysis but also helps to deepen the understanding of community change in complex temporal networks.

Problem

• Dynamic Networks
• Community Structure
• Temporal Evolution

Innovation

• Normalized Mutual Information
• Dynamic Network Analysis
• Community Structure Similarity