Abstract
There are many problems in the community detection algorithm based on big data sets that can’t effectively find the overlapping community and the rationality of the community structure is not high. This paper proposes overlapping community detection based on the connection similarity of maximum clique. The algorithm introduces an idea of maximum clique to initialize the network structure, quantifying analysis the community connection similarity, which is based on the sharing neighbor nodes and the connection between communities. On this basis, all cliques are merged to get a rational structure of overlapping community. The rationality of the proposed algorithm is tested on four real network dataset through comparing with CPM algorithm. The results show that the proposed algorithm has superior performance in term of accuracy, coverage and modularity on mining community structure. Thus, this algorithm is a kind of effective overlapping community detection algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chopade, P., Zhan, J.: Community detection in large-scale big data networks. In: ASE International Conference, pp. 1–7 (2014)
Gaigai, G., Yuhua, Q., Xiaoqin, Z., et al.: Algorithm of detecting community in bipartite network with autonomous determination of the number of communities. Pattern Recognit. Artif. Intell. 28(11), 969–975 (2015)
Liwei, H., Deyi, L., Yutao, M., et al.: A meta path-based link prediction model for heterogeneous information networks. Chin. J. Comput. 37(4), 848–858 (2014)
Ruiyang, H., WuQi, Z.Y., et al.: Community detection in dynamic heterogeneous network with joint nonnegative matrix factorization. Appl. Res. Comput. 34(10), 2989–2992 (2017)
Hu, J., Wang, B., Lee, D.: Research on centrality of node importance in scale-free complex networks. In: Proceedings of the 31st Chinese Control Conference, China, pp. 1073–1077 (2012)
Kitsak, M., Havlin, S., Paul, G., et al.: Betweenness centrality of fractal and nonfractal scale-free model networks and tests on real networks. Phys. Rev. E 75(5), 056115 (2007)
Lancichinetti, A., Fortunato, S., Kertész, J.: Detecting the overlapping and hierarchical community structure of complex networks. New J. Phys. 11(3), 19–44 (2018)
Xiaojia, L., Peng, Z., Zengru, D., et al.: Community structure in complex networks. Complex Systems and Complexity Science 5(3), 19–42 (2008)
Scott, J., Hughes, M., Mackenzie, J.: The anatomy of scottish capital: Scottish companies and scottish capital, 1900–79. Econ. Hist. Rev. 33(4) (1980)
Sun, Y., Norick, B., Han, J., et al.: Integrating meta-path selection with user-guided object clustering in heterogeneous information networks. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1348–1356. ACM (2012)
Tang, L., Liu, H.: Community detection and mining in social media. Morgan & Claypool, San Rafael (2010). 137
Qi, W., Fucai, C., Ruiyang, H., et al.: Community detection in heterogeneous network with semantic paths. Acta Electron. Sinica 44(6), 1465–1471 (2016)
Yajing, W., Zengru, D., Ying, F.: A Clustering algorithm for bipartite network based on distribution matrix of resources. J. Beijing Norm. Univ. Nat. Sci. 5, 643–646 (2010)
Yuanzhuo, W., Xiaolong, J., Xueqi, C.: Network big data; present and future. Chin. J. Comput. 36(6), 1125–1138 (2013)
Wei, D., Li, Y., Zhang, Y., et al.: Degree centrality based on the weighted network. In: 2012 24th Chinese Control and Decision Conference (CCDC), China, pp. 3976–3979. IEEE (2012)
Tao, W., Yang, L., Yaoyi, X., et al.: Identifying community in bipartite networks using graph regularized-based non-negative matrix factorization. J. Electron. Inf. Technol. 37(9), 2238–2245 (2015)
Zhang, P., Wang, J., Li, X., et al.: Clustering coefficient and community structure of bipartite networks. Phys. A Stat. Mech. Appl. 387(27), 6869–6875 (2007)
Acknowledgement
This article is supported by National Natural Science Foundation of China under grant No. 71461017.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Qian, X., Yang, L., Fang, J. (2018). Overlapping Community Detection Based on Community Connection Similarity of Maximum Clique. In: Zhou, Q., Gan, Y., Jing, W., Song, X., Wang, Y., Lu, Z. (eds) Data Science. ICPCSEE 2018. Communications in Computer and Information Science, vol 901. Springer, Singapore. https://doi.org/10.1007/978-981-13-2203-7_18
Download citation
DOI: https://doi.org/10.1007/978-981-13-2203-7_18
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-2202-0
Online ISBN: 978-981-13-2203-7
eBook Packages: Computer ScienceComputer Science (R0)