Abstract
In this paper, we propose a community search scheme via attribute subspace. This method utilizes not only network structure but also node attributes within a certain subspace to quantify a community from the perspective of both internal consistency and external separability, which is able to capture a user preferred community. Firstly, the attributes similarity and neighborhood information of nodes are combined, and the center node set of the target community can be obtained by extending the sample node given by the user with its neighbors. Secondly, an attribute subspace calculation method with entropy weights is established based on the center node set, and the attribute subspace of the community can thus be deduced. Finally, the community quality, which is the combination of internal connectivity and external separability is defined, based on which the target community with user’s preference can be detected. Experimental results on both synthetic network and real-world network datasets demonstrated the efficiency and effectiveness of the proposed algorithm.
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Ma, X., Dong, D., Wang, Q.: Community detection in multi-layer networks using joint nonnegative matrix factorization. IEEE Trans. Knowl. Data Eng. 31(2), 273–286 (2019)
Cheng, K., Li, J., Liu, H.: Unsupervised feature selection in signed social networks. In: Proceedings of the 17th International ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 777–786 (2017)
Wu, W., Kwong, S., Zhou, Y., Jia, Y., Gao, W.: Nonnegative matrix factorization with fixed hypergraph regularization for community detection. Inform. Sci. 435, 263–281 (2018)
Ding, X., Zhang, J., Yang, J.: A robust two-stage algorithm for local community detection. Knowl.-Based Syst. 152, 188–199 (2018)
Boorman, S., White, H.: Social structure from multiple networks. II. Role structures. Am. J. Sociol. 81(6), 1384–1446 (1976)
Baumes, J., Goldberg, M., Krishnamoorthy, M., Magdon-Ismail, M., Preston, N.: Finding communities by clustering a graph into overlapping subgraph. In: Proceedings of the IADIS International Conference on Applied Computing, pp. 97–104 (2005)
Lancichinetti, A., Radicchi, F., Ramasco, J., Fortunato, S.: Finding statistically significant communities in networks. PLoS One 6(4), e18961 (2011)
Perozzi, B., Akoglu, L., Sánchez, P., Müller, E.: Focused clustering and outlier detection in large attributed graphs. In: Proceedings of the 14th International ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 1346–1355 (2014)
Wu, P., Pan, L.: Mining target attribute subspace and set of target communities in large attributed networks (2017)
Liu, H., Ma, H., Chang, Y., Li, Z.: Target community detection based on attribute subspace with entropy weighting. Chin. Inform. Process. 33(8), 114–123 (2019)
Li, X., Wu, Y., Ester, M., Kao, B., Wang, X., Zheng, Y.: Semi-supervised clustering in attributed heterogeneous information networks. In: Proceedings of the 26th International World Wide Web Conference on World Wide Web, pp. 1621–1629 (2017)
Günnemann, S., Färber, I., Raubach, S., Seidl, T.: Spectral subspace clustering for graphs with feature vectors. In: Proceedings of the 14th International on Data Mining, pp. 231–240 (2013)
Chen, F., Zhou, B., Alim, A., Zhao, L.: A generic framework for interesting subspace cluster detection in multi-attributed networks. In: Proceedings of the 17th IEEE International Conference on Data Mining, pp. 41–50 (2017)
Jing, L., Ng, M., Huang, J.: An entropy weighting k-means algorithm for subspace clustering of high-dimensional sparse data. IEEE Trans. Knowl. Data Eng. 19(8), 1026–1041 (2007)
Perozzi, B., Akoglu, L.: Scalable anomaly ranking of attributed neighborhoods. In: Proceedings of the 3rd International Conference on Sustainable Design and Manufacturing, pp. 207–215 (2016)
Acknowledgment
The work is supported by the National Natural Science Foundation of China (No. 61762078, 61363058, 61663004) Guangxi Key Laboratory of Trusted Software (No. kx201910) and Research Fund of Guangxi Key Lab of Multi-source Information Mining & Security (MIMS18-08).
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Liu, H., Ma, H., Chang, Y., Li, Z., Wu, W. (2019). Leveraging User Preferences for Community Search via Attribute Subspace. In: Douligeris, C., Karagiannis, D., Apostolou, D. (eds) Knowledge Science, Engineering and Management. KSEM 2019. Lecture Notes in Computer Science(), vol 11775. Springer, Cham. https://doi.org/10.1007/978-3-030-29551-6_52
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DOI: https://doi.org/10.1007/978-3-030-29551-6_52
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