Abstract
Communities in a network are groups of nodes that are more strongly connected to each other. This article proposes a novel method for community detection in dynamic networks, focusing on influential nodes and overlapping communities. The method, named community detection based on adaptive multi-centrality aggregation (CDAMA), tackles two key challenges identifying influential nodes and overlapping communities. CDAMA introduces the Adaptive multi-centrality aggregation (AMCA) approach to identify influential nodes. AMCA integrates multiple centrality measures. The adaptive overlap control and merging (AOC-CM) approach addresses overlapping communities. AOC-CM utilizes structural, temporal, and semantic factors to strategically merge communities while preserving those with minimal overlap. CDAMA consists of five phases: receiving network snapshots, selecting influential nodes, launching communities, checking overlap and merging communities, and updating communities. Evaluation on three benchmark datasets demonstrates that CDAMA outperforms existing state-of-th-art methods in terms of Newman modularity, Modularity with split penalty and density modularity and Execution time. This suggests CDAMA is a valuable tool for tasks like viral marketing, information diffusion analysis, and network resilience studies.



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The datasets analyzed during the current study are available through: Cit-Hep Ph: https://snap.stanford.edu/data/cit-HepPh.html. Cit—Hep Th: https://snap.stanford.edu/data/cit-HepTh.html. sx-mathoverflow: https://snap.stanford.edu/data/sx-mathoverflow.html. CollegeMsg: https://snap.stanford.edu/data/CollegeMsg.html.
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Marjan Mokhtari conceptualized the main research question and conducted a literature review on existing community detection methods and influence maximization techniques and drafted the background and related work sections of the manuscript. Meimanat Dadras formulated the mathematical framework for identifying influential nodes in dynamic networks and developed and implemented the proposed community detection algorithm based on influential nodes. Mahdi Kherad provided guidance and expertise in dynamic network analysis and performed the experiments and analyzed the results, including data collection and visualization and edited the manuscript, ensuring clarity and scientific rigor.
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Kherad, M., dadras, M. & Mokhtari, M. Community detection based on influential nodes in dynamic networks. J Supercomput 80, 24664–24688 (2024). https://doi.org/10.1007/s11227-024-06367-4
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DOI: https://doi.org/10.1007/s11227-024-06367-4