Abstract
Community detection is a significant but challenging task in the field of social network analysis. Many effective approaches have been proposed to solve this issue. However, most of them are mainly based on the topological structure or node features. In this study, we consider both these two aspects to detect non-overlapping and overlapping communities. Specifically, we define a novel quality metric based on closed topology and feature triangles. When this metric is used as an objective function, we propose a local learning framework to optimize it to achieve different community detection tasks. Extensive experiments on real-world social networks demonstrate that our framework achieves satisfactory results compared with other baseline approaches.
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Acknowledgements
This work was partially supported by the High-Level Introduction of Talent Scientific Research Start-up Fund of Jiangsu Police Institute under Grant JSPI21GKZL401, and the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province, China (No.21KJB520034).
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Gao, G., Sun, A., Gu, H. (2022). Community Detection Based on Topology and Node Features in Social Networks. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2022. Lecture Notes in Computer Science, vol 13339. Springer, Cham. https://doi.org/10.1007/978-3-031-06788-4_24
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