Abstract
Community detection is a fundamental tool to uncover organizational principles in complex networks. With the proliferation of rich information available for real-world networks, it is useful to detect communities in attributed networks. Recently, many algorithms consider combinating node attributes and network topology, and the effect of these methods is better than using only one information source. However, the existing algorithms still have some shortcomings. First, only a few algorithms can process both categorical type and numerical type at the same time. Second, the contribution between attributes and topology cannot be adjusted adaptively. Third, most algorithms do not consider combining the high-order structure with the node attributes. Therefore, we propose an adaptive seed expansion algorithm based on composite similarity to solve these problems(ASECS). We generate a new weighted KNN graph according to the composite similarity. The composite similarity combines high-order structure similarity, low-order structure similarity and attributed similarity by weighting them. Our method can adaptively adjust the contribution between topology and node attributes. Moreover, the designed attributed similarity function can process both categorical and numerical attributes. Finally, we find the seed nodes on the weighted KNN graph and expand the seed nodes to communities. The superiority of our algorithm is demonstrated on many networks.
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Chen, W., Guo, K., Chen, Y. (2022). Adaptive Seed Expansion Based on Composite Similarity for Community Detection in Attributed Networks. In: Sun, Y., et al. Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2021. Communications in Computer and Information Science, vol 1492. Springer, Singapore. https://doi.org/10.1007/978-981-19-4549-6_17
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DOI: https://doi.org/10.1007/978-981-19-4549-6_17
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