Abstract
Community detection algorithms are the basic tools for discovering the internal structure and organizational principles of a community. Ranging from model-based and optimization-based methods, existing efforts typically consider two sources of information, i.e. network structure and node attributes, to obtain communities with both denser network structure and similar attribute information. We argue that an inherent drawback of such methods is that, different impacts of different sources, is ignored during the clustering process. Besides, some existing community detection algorithms typically consider two sources of information but they cannot automatically determine the relative importance between them to reveal subspaces. As such, the detected communities may be unsatisfactory.
In this work, we propose to integrate subspace into a new overlapping community detection framework, an Overlapping Community Detection with Subspaces on Double-Views (CDDV), which exploits the relative importance between structures and attributes. This leads to a better detection result, effectively injecting subspaces to show the diversity of communities into the detection process in an explicit manner. We conduct extensive experiments on four public benchmarks, demonstrating significant improvements over several state-of-the-art models. Further analysis verifies the importance of subspace finding for capturing better communities, justifying the rationality and effectiveness of CDDV.
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Acknowledgment
This work is supported by the National Natural Science Foundation of China (61762078, 61363058, 61966004, 61862058), Research Fund of Guangxi Key Lab of Multi-source Information Mining and Security (MIMS18-08), Northwest Normal University young teachers research capacity promotion plan (NWNU-LKQN2019-2) and Research Fund of Guangxi Key Laboratory of Trusted Software (kx202003).
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Chang, Y., Ma, H., Su, Y., Li, Z. (2020). An Overlapping Community Detection with Subspaces on Double-Views. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Lecture Notes in Computer Science(), vol 12533. Springer, Cham. https://doi.org/10.1007/978-3-030-63833-7_14
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DOI: https://doi.org/10.1007/978-3-030-63833-7_14
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