Abstract
Communities exist anywhere in various complex networks, and community evolution tracking is one of the most well-liked areas of inquiry in the study of dynamic complex networks. Community evolution tracking has many applications in daily life, such as predicting social network behaviors or analyzing the spread of infectious illnesses. However, the majority of existing evolution tracking algorithms obtain community detection results before matching the community in tracking evolution events, making it difficult to trace the whole evolution of communities because of community matching errors. In addition, the majority of evolution tracking algorithms do not adequately account for the potential scenarios in community evolution, resulting in erroneous detection of evolution events. In this research, a community evolution tracking algorithm based on edge variation discerning and core node extension is proposed. First, we detect communities based on the core node extension strategy, which avoids the problem of community matching errors. Second, we track community evolution based on the edge variation discerning strategy, which fully considers various situations that may occur during the community evolution process. According to the outputs of our experiments, our system can effectively track the evolution of communities in synthetic dynamic networks.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China under Grant No. 62002063 and No. U21A20472, the National Key Research and Development Plan of China under Grant No. 2021YFB36 00503, the Fujian Collaborative Innovation Center for Big Data Applications in Governments, the Fujian Industry-Academy Cooperation Project under Grant No. 2017H6008 and No. 2018H6010, the Natural Science Foundation of Fujian Province under Grant No. 2020J05112 and No. 2020J01420, the Fujian Provincial Department of Education under Grant No. JAT190026, the Major Science and Technology Project of Fujian Province under Grant No. 2021HZ022007 and Haixi Government Big Data Application Cooperative Innovation Center.
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Zhuang, Q., Yu, Z., Guo, K. (2023). Community Evolution Tracking Based on Core Node Extension and Edge Variation Discerning. In: Sun, Y., et al. Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2022. Communications in Computer and Information Science, vol 1681. Springer, Singapore. https://doi.org/10.1007/978-981-99-2356-4_12
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DOI: https://doi.org/10.1007/978-981-99-2356-4_12
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