Abstract
Due to the sparsity of network, some community detection methods only based on topology often lead to relatively low accuracy. Although some methods have been proposed to improve the detection accuracy by using few known semi-supervised information or node content, the research of community detection not only pursues the enhancement of community accuracy, but also pays more attention to the semantic description for communities. In this paper, we proposed a unified nonnegative matrix factorization framework simultaneously for community detection and semantic matching by integrating both semi-supervised information and node content. The framework reveals two-fold community structures as well as their coupling relationship matrix, which helps to identify accurate community structure and at the same time assign specific semantic information to each community. Experiments on some real networks show that the framework is efficient to match each community with specific semantic information, and the performance are superior over the compared methods.
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Acknowledgments
This work was supported by the Major Project of National Social Science Fund(14ZDB153), the major research plan of the National Natural Science Foundation (91746205,91746107,91224009,51438009), and the Natural Science Foundation of China (61772361).
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Wang, W., Liu, X., Jiao, P., Chen, X., Jin, D. (2018). A Unified Weakly Supervised Framework for Community Detection and Semantic Matching. In: Phung, D., Tseng, V., Webb, G., Ho, B., Ganji, M., Rashidi, L. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 10939. Springer, Cham. https://doi.org/10.1007/978-3-319-93040-4_18
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DOI: https://doi.org/10.1007/978-3-319-93040-4_18
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