Temporal Community Detection and Analysis with Network Embeddings

Limengzi Yuan, Xuanming Zhang, Yuxian Ke, Zhexuan Lu, Xiaoming Li () and Changzheng Liu ()
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Limengzi Yuan: College of Information Science and Technology, Shihezi University, Xinjiang 832003, China
Xuanming Zhang: College of Information Science and Technology, Shihezi University, Xinjiang 832003, China
Yuxian Ke: College of Information Science and Technology, Shihezi University, Xinjiang 832003, China
Zhexuan Lu: College of Information Science and Technology, Shihezi University, Xinjiang 832003, China
Xiaoming Li: College of International Business, Zhejiang Yuexiu University, Shaoxing 312000, China
Changzheng Liu: College of Information Science and Technology, Shihezi University, Xinjiang 832003, China

Mathematics, 2025, vol. 13, issue 5, 1-22

Abstract: As dynamic systems, social networks exhibit continuous topological changes over time, and are typically modeled as temporal networks. In order to understand their dynamic characteristics, it is essential to investigate temporal community detection (TCD), which poses significant challenges compared to static network analysis. These challenges arise from the need to simultaneously detect community structures and track their evolutionary behaviors. To address these issues, we propose TCDA-NE, a novel TCD algorithm that combines evolutionary clustering with convex non-negative matrix factorization (Convex-NMF). Our method innovatively integrates community structure into network embedding, preserving both microscopic details and community-level information in node representations while effectively capturing the evolutionary dynamics of networks. A distinctive feature of TCDA-NE is its utilization of a common-neighbor similarity matrix, which significantly enhances the algorithm’s ability to identify meaningful community structures in temporal networks. By establishing coherent relationships between node representations and community structures, we optimize both the Convex-NMF-based representation learning model and the evolutionary clustering-based TCD model within a unified framework. We derive the updating rules and provide rigorous theoretical proofs for the algorithm’s validity and convergence. Extensive experiments on synthetic and real-world social networks, including email and phone call networks, demonstrate the superior performance of our model in community detection and tracking temporal network evolution. Notably, TCDA-NE achieves a maximum improvement of up to 0.1 in the normalized mutual information (NMI) index compared to state-of-the-art methods, highlighting its effectiveness in temporal community detection.

Keywords: temporal community detection; social networks; network embedding; evolutionary clustering; convex non-negative matrix factorization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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