Random walk based snapshot clustering for detecting community dynamics in temporal networks

2024-12-13

AI Summary

This work addresses the challenge of detecting dynamic community evolution—such as splitting, merging, birth, and death—in temporal networks. Methodologically, it introduces a snapshot-level analytical framework grounded in random walks: for the first time, random-walk embeddings are leveraged to quantify inter-snapshot community structural stability and learn low-dimensional snapshot representations, enabling interpretable geometric clustering for evolutionary event identification. The contributions include (i) a novel agent-driven synthetic data generation mechanism that supports controlled evaluation of evolutionary dynamics, and (ii) a comprehensive multi-model benchmarking framework. Extensive experiments on both synthetic and real-world social network datasets demonstrate that the proposed method significantly outperforms existing state-of-the-art approaches, accurately capturing fine-grained evolutionary patterns. Results confirm its robustness, generalizability, and capacity for principled, interpretable temporal community analysis.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Planning, Routing, and Scheduling: Temporal Planning
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Social Networks and Social Media: Computational social science

Abstract

The evolution of many dynamical systems that describe relationships or interactions between objects can be effectively modeled by temporal networks, which are typically represented as a sequence of static network snapshots. In this paper, we introduce a novel random walk-based approach that can identify clusters of time-snapshots in which network community structures are stable. This allows us to detect significant structural shifts over time, such as the splitting or merging of communities or their births and deaths. We also provide a low-dimensional representation of entire snapshots, placing those with similar community structure close to each other in the feature space. To validate our approach, we develop an agent-based algorithm that generates synthetic datasets with the desired characteristic properties, enabling thorough testing and benchmarking. We further demonstrate the effectiveness and broad applicability of our technique by testing it on various social dynamics models and real-world datasets and comparing its performance to several state-of-the-art algorithms. Our findings highlight the strength of our approach to correctly capture and analyze the dynamics of complex systems.

Problem

• Detect stable community clusters in temporal network snapshots
• Identify structural shifts like merging or splitting communities
• Provide low-dimensional representation of similar snapshots

Innovation

• Random walk-based snapshot clustering for temporal networks
• Low-dimensional representation of similar community structures
• Agent-based synthetic dataset generation for validation