Abstract
Recently, most of the community discovery algorithms are based on the structural information of undirected networks, and the social characteristics of users are less considered. Based on the academic social network, we propose a label propagation algorithm that integrates the network structure and multi-dimensional user information (LPA-NU). Through the fusion of multi-dimensional social networks, the algorithm firstly uses the LDA model to mine the similarity of user research directions to derive the hidden social edges between users. Secondly, it constructs a comprehensive directed weighted network, and then classifies the community according to the initial sub-group information. In order to evaluate the quality of community discovery, this paper proposes the definition of overlapping modules of directed networks. We conduct relevant experiments on real social network datasets (SCHOLAT). Experiments show that the LPA-NU algorithm can better divide the structure of the community, and the quality of community division is higher.
Keywords
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Ying, K., Gu, X., Bo, Y., et al.: A multilevel community detection algorithm for large-scale social information networks. Chin. J. Comput. (1), 169–182 (2016)
Li, J., Zhou, Z, R.: Community discovery of P2P resources based on bipartite graph. In: International Conference on Computational Intelligence & Software Engineering (2009)
Qi, J., Liang, X., Yi, W.: Overlapping community detection algorithm based on selection of seed nodes. Appl. Res. Comput. (12), 20–23 + 54 (2017)
Lai, D., Lu, H., Nardini, C.: Finding communities in directed networks by PageRank random walk induced network embedding. Phys. Stat. Mech. Appl. 389(12), 2443–2454 (2010)
Wang, Z., He, M., Du, Y.: Text similarity computing based on topic model LDA. Comput. Sci. 40(12), 229–232 (2013)
Gregory, S.: Finding overlapping communities in networks by label propagation. New J. Phys. 12(10), 103018 (2010)
Zhen, W., Che, C., Qian, Y., et al.: A two-stage community detection algorithm based on label propagation. J. Comput. Res. Dev. 55(09), 135–147 (2018)
Liu, J., Xu, B., Xu, X., et al.: A link prediction algorithm based on label propagation. J. Comput. Sci. 16, 43–50 (2016). S1877750316300382
Du, C., Wang, Z., Xing, Z.: Overlapping community detection algorithm based on improved multi-label propagation. J. Data Acquisition Process. 33(2), 288–298 (2018)
Liu, S., Zhu, F., Gan, L.: A label-propagation-probability-based algorithm for overlapping community detection. Chin. J. Comput. 39(4), 717–729 (2016)
Fei, Y., Ming, Z., Yuwei, T., et al.: Community discovery based on actors’ interests and social network structure. J. Comput. Res. Dev. 47, 357–362 (2010)
Yu, X., Jing, Y., Tang, C., et al.: An overlapping semantic community detection algorithm based on local semantic cluster. J. Comput. Res. Dev. 52(7), 1510–1521 (2015)
Nicosia, V., Mangioni, G., Carchiolo, V., et al.: Extending the definition of modularity to directed graphs with overlapping communities. J. Stat. Mech.: Theory Exp. 3, 3166–3168 (2009)
Han, Z., Chen, Y., Liu, W., et al.: Research on node innuence analysis in social networks. J. Softw. 28(1), 84–104 (2017)
Huang, C., Yin, J., Hou, F.: A text similarity measurement combining word semantic information with TF—IDF method. Chin. J. Comput. 34(5), 856–864 (2011)
Luo, J., Wang, Q., Li, Y.: Word clustering based on word2vec and semantic similarity. In: 33rd Chinese Control Conference (CCC), pp. 508–511. IEEE (2014)
Dong, Y., Li, W., Yu, H.: Hierarchical relation mining of Chinese text based on mixed cosine similarity. Appl. Res. Comput. 34(5), 1406–1409 (2017)
Shen, H., Cheng, X., Cai, K., et al.: Detect overlapping and hierarchical community structure in networks. Phys.: Stat. Mech. Appl. 388(8), 1706–1712 (2009)
Acknowledgements
Our works were supported by the National Natural Science Foundation of China (No. U1811263, No. 61772211) and Innovation Team in Guangdong Provincial Department of Education (No. 2018-64/8S0177).
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Wang, L., He, Y., Mao, C., Mao, D., Yang, Z., Li, Y. (2019). Research on Community Discovery Algorithm Based on Network Structure and Multi-dimensional User Information. In: Sun, Y., Lu, T., Yu, Z., Fan, H., Gao, L. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2019. Communications in Computer and Information Science, vol 1042. Springer, Singapore. https://doi.org/10.1007/978-981-15-1377-0_33
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DOI: https://doi.org/10.1007/978-981-15-1377-0_33
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