Abstract
Detecting community structure is an important research topic in complex network analysis. How to improve community detection results by using various features in the network is a very challenging problem. The scale-free and attributes of nodes are the two relatively independent aspects of the complex networks in the real world, the former is an inherent structural feature from the global perspective and the later can be used to significantly enhance community detection and community semantics. However, these two aspects are usually modeled and computed independently in previous methods. Based on that, we propose a novel unified Bayesian generative model which combines network topology and node attributes simultaneously to identify community structures via considering to model the scale-free feature. We propose the degree decay variable to preserve the power-law degree characteristic of the network. Specifically, this model composes of two closely correlated parts by a probabilistic transition matrix, one for network topology and the other for nodes attributes. Moreover, we develop a variational EM algorithm to optimize the objective function of the model. Experiments on synthetic and real networks show that our model has a better performance compared with some baselines on community detection in attribute networks.
S. Zhang and L. Pan—Equal contribution.
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Brian, K., Newman, M.E.J.: Stochastic blockmodels and community structure in networks. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 83(2), 016107 (2011)
Chakraborty, Y.P.N., Sycara, K.: Nonnegative matrix tri-factorization with graph regularization for community detection in social networks. In: Twenty-Fourth International Joint Conference on Artificial Intelligence, pp. 2083–2089. AAAI Press (2015)
Chang, Z., Yin, X., Jia, C., Wang, X.: Mixture models with entropy regularization for community detection in networks. Phys. A: Stat. Mech. Appl. 496, 339–350 (2018)
Chen, Y., Wang, X., Bu, J., Tang, B., Xiang, X.: Network structure exploration in networks with node attributes. Phys. A: Stat. Mech. Appl. 449, 240–253 (2016)
Fortunato, S., Hric, D.: Community detection in networks: a user guide. Phys. Rep. 659, 1–44 (2016)
He, D., Feng, Z., Jin, D., Wang, X., Zhang, W.: Joint identification of network communities and semantics via integrative modeling of network topologies and node contents. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)
He, D., Yang, X., Feng, Z., Chen, S., Fogelman-Soulié, F.: A network embedding-enhanced approach for generalized community detection. In: Liu, W., Giunchiglia, F., Yang, B. (eds.) KSEM 2018. LNCS (LNAI), vol. 11062, pp. 383–395. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99247-1_34
Hua-Wei, S., Xue-Qi, C., Jia-Feng, G.: Exploring the structural regularities in networks. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 84(2), 056111 (2011)
Jin, D., Wang, K., Zhang, G., Jiao, P., He, D., Fogelman-Soulie, F., Huang, X.: Detecting communities with multiplex semantics by distinguishing background, general and specialized topics. IEEE Trans. Knowl. Data Eng. 32, 2144–2158 (2019)
Karrer, B., Newman, M.E.: Stochastic blockmodels and community structure in networks. Phys. Rev. E 83(1), 016107 (2011)
Newman, M.E.J., Leicht, E.A.: Mixture models and exploratory analysis in networks. Proc. Natl. Acad. Sci. USA 104(23), 9564–9569 (2007)
Pool, S., Bonchi, F., Leeuwen, M.V.: Description-driven community detection. ACM Trans. Intell. Syst. Technol. (TIST) 5(2), 28 (2014)
Qiao, M., Yu, J., Bian, W., Li, Q., Tao, D.: Adapting stochastic block models to power-law degree distributions. IEEE Trans. Cybern. 49(2), 626–637 (2019)
Ruan, Y., Fuhry, D., Parthasarathy, S.: Efficient community detection in large networks using content and links. In: International Conference on World Wide Web, pp. 1089–1098 (2013)
Wang, X., Jin, D., Cao, X., Yang, L., Zhang, W.: Semantic community identification in large attribute networks. In: Thirty-Second AAAI Conference on Artificial Intelligence, pp. 265–271 (2016)
Yang, T., Jin, R., Chi, Y., Zhu, S.: Combining link and content for community detection. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 927–936 (2009)
Yu, Z., Zhu, X., Wong, H.S., You, J., Zhang, J., Han, G.: Distribution-based cluster structure selection. IEEE Trans. Cybern. 47(11), 3554–3567 (2017)
Acknowledgments
This work was supported by the National Natural Science Foundation of China (61902278), the National Key R&D Program of China (2018YFC0832101), the Tianjin Science and Technology Development Strategic Research Project under Grant (18ZXAQSF00110) and National Social Science Foundation of China (15BGL035).
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Zhang, S., Wang, Y., Wang, W., Jiao, P., Pan, L. (2021). A Unified Bayesian Model of Community Detection in Attribute Networks with Power-Law Degree Distribution. In: Gao, H., Wang, X., Iqbal, M., Yin, Y., Yin, J., Gu, N. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 350. Springer, Cham. https://doi.org/10.1007/978-3-030-67540-0_34
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DOI: https://doi.org/10.1007/978-3-030-67540-0_34
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