Abstract
Social networks are usually modeled as signed networks. The community detection is an important problem for the research of signed networks. The time complexity of signed label propagation algorithm is lower than most existing algorithms for community detection in the signed networks. However, bad performance on robustness and accuracy in the algorithm should not be ignored. Thus, we propose a structural balance degree to measure the balance of an edge in the local network and the local network density. Then a novel signed network label propagation algorithm with structural balance degree is proposed for community detection in signed networks. Besides, the algorithm is tested on several real-world social networks. Experimental results prove that the optimized algorithm can enhance both the robustness and the effectiveness. Its convergence rate is also faster than current algorithms.
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References
Girvan, M., Newman, M.E.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99, 7821–7826 (2002)
Newman, M.E.: The structure and function of complex networks. SIAM Rev. 45, 167–256 (2003)
Dev, H.: A user interaction based community detection algorithm for online social networks. In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, pp. 1607–1608 (2014)
Lin, W.Q., Kong, X.N., Yu, P.S., Wu, Q.Y., Jia, Y., Li, C.: Community detection in incomplete information networks. In: Proceedings of the 21st International Conference on World Wide Web, pp. 341–350 (2012)
Jin, H., Wang, S.L., Li, C.Y.: Community detection in complex networks by density-based clustering. Phys. A Stat. Mech. Appl. 392, 4606–4618 (2013)
Barbieri, N., Bonchi, F., Manco, G.: Cascade-based community detection. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, pp. 33–42 (2013)
Xia, Z.Y., Bu, Z.: Community detection based on a semantic network. Knowl. Inf. Syst. 26, 30–39 (2012)
Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E 76, 036106 (2007)
Šubelj, L., Bajec, M.: Unfolding communities in large complex networks: combining defensive and offensive label propagation for core extraction. Phys. Rev. E 83, 885–896 (2011)
Šubelj, L., Bajec, M.: Robust network community detection using balanced propagation. Phys. Condens. Matter 81, 353–362 (2011)
Heider, F.: Attitudes and cognitive organization. J. Psychol. 21, 107–112 (1946)
Nakanishi, H., Nakazawa, S., Ishida, T., et al.: Can software agents influence human relations?: balance theory in agent-mediated communities. In: International Joint Conference on Autonomous Agents & Multiagent Systems, AAMAS 2003, 14–18 July 2003, Melbourne, Victoria, Australia, Proceedings, pp. 717–724 (2003)
Sergio, G., Pablo, J., Alex, A.: Analysis of community structure in networks of correlated data. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 80, 016114 (2009)
Traag, V.A., Bruggeman, J.: Community detection in networks with positive and negative links. Phys. Rev. E 80, 036115 (2009)
Li, X., Chen, H., Li, S.: Exploiting emotions in social interactions to detect online social communities. In: PACIS (2010)
Bansal, N., Blum, A., Chawla, S.: Correlation clustering. Mach. Learn. 56, 89–113 (2004)
Yang, B., Cheung, W.K., Liu, J.: Community mining from signed social networks. IEEE Trans. Knowl. Data Eng. 19, 1333–1348 (2007)
Sharma, T., Charls, A., Singh, P.K.: Community mining in signed social networks—an automated approach. In: Proceedings of the International Conference on Computer Engineering and Applications, pp. 152–157. IACSIT Press, Singapore (2009)
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Fang, L., Yang, Q., Wang, J., Lei, W. (2016). Signed Network Label Propagation Algorithm with Structural Balance Degree for Community Detection. In: Chang, C., Chiari, L., Cao, Y., Jin, H., Mokhtari, M., Aloulou, H. (eds) Inclusive Smart Cities and Digital Health. ICOST 2016. Lecture Notes in Computer Science(), vol 9677. Springer, Cham. https://doi.org/10.1007/978-3-319-39601-9_38
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DOI: https://doi.org/10.1007/978-3-319-39601-9_38
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