Community Structure Analysis in Social Network of Sina Weibo

Abstract:

Online social network is different from the traditional social network. it has distinctive characteristics, such as openness, anonymity, across the region, a high degree of interactivity and complexity. In this paper, community structure analysis in social network of Sina Weblog is analyzed and discussed based on directed network. According to the characteristics of Sina Weblog, We first constructed three kinds of network (such as follow-network, fan-network and all-network) and discussed degree distribution of weibo users. Then, structure and characteristics of Sina Weblog social network community is discussed and analyze based on the three network types on the. The experiments show that part and integral has the same properties, Degree distribution obeys power-law distribution, community has a small world and users are in accordance with six degrees of separation theory in Sina Weblog community. These research results verify that Sina Weblog has the structural characteristics of on line social relation network.

• Periodical: Applied Mechanics and Materials (Volume 590)
• Pages: 756-762
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.590.756
• Online since: June 2014
• Authors: Sheng Wu Xu*, Xia Zhang, Zheng You Xia
• Keywords: All-Network, Community, Fan-Network, Follow-Network, Sina Weblog, Social Relation Network

References

[1] Hu Hai-Bo, Xu Ling, Wang Ke, et al. Analysis of large-scale online social network structure[J]. Journal of Shanghai Jiao Tong University, 2009 (4): 587-591(in Chinese).

[2] Zhan Bu, Zhengyou Xia , Wang J, et al. A last updating evolution model for online social networks[J]. Physica A: Statistical Mechanics and its Applications, Volume 392, Issue 9, 1 May 2013, Pages 2240–2247.

DOI: 10.1016/j.physa.2013.01.006

[3] Kumar R, Novak J, Tomkins A. Structure and evolution of online social networks[M]/Link Mining: Models, Algorithms, and Applications. Springer New York, 2010: 337-357.

DOI: 10.1007/978-1-4419-6515-8_13

[4] ZhengYou Xia, Bu Zhan. Community detection based on a semantic network[J]. Knowledge-Based Systems, 2012, 26: 30-39.

DOI: 10.1016/j.knosys.2011.06.014

[5] Grabowicz P A, Ramasco J J, Eguiluz V M. Dynamics in online social networks[J]. arXiv preprint arXiv: 1210. 0808, (2012).

[6] Ferrara E. A large-scale community structure analysis in Facebook[J]. EPJ Data Science, 2012, 1(1): 1-30.

[7] E Ferrara, P De Meo, G Fiumara, A Provetti. The role of strong and weak ties in Facebook: a community structure perspective. Available at http: /arxiv. org/abs/1203. 0535.

[8] Granovetter M (1973) The strength of weak ties. Am J Sociology 78: 1360–1380.

[9] Dou Bing-Lin, Li Shu-Song, Zhang Shi-Yong. Analysis of the structure of the social network based on. Chinese Journal of computers, 2012, 35(4): 741-753 (in Chinese).

DOI: 10.3724/sp.j.1016.2012.00741

[10] Zhang P, Yue K, Li J, et al. Detecting Community Structures in Microblogs from Behavioral Interactions[M]/Web Technologies and Applications. Springer Berlin Heidelberg, 2013: 734-745.

DOI: 10.1007/978-3-642-37401-2_71

[11] Zhan Bu, Zhengyou Xia , Wang J. A sock puppet detection algorithm on virtual spaces[J]. Knowledge-Based Systems, Volume 37, January 2013, Pages 366–377.

DOI: 10.1016/j.knosys.2012.08.016

[12] Zhao Z, Feng S, Wang Q, et al. Topic oriented community detection through social objects and link analysis in social networks[J]. Knowledge-Based Systems, 2012, 26: 164-173.

DOI: 10.1016/j.knosys.2011.07.017

[13] Tian Zhan-Wei, Sui Yang. An empirical analysis of micro-blog information communication based on complex network theory[J]. Library and information work, 2012, 8: 42-46(in Chinese).

[14] Chen Ke-Han, Han Pan-Pan, Wu Jian. Heterogeneous social networks recommendation algorithm based on user clustering[J]. Chinese Journal of computers, 2013, 2: 013 (in Chinese).

[15] Raghavan U N, Albert R, Kumara S. Near linear time algorithm to detect community structures in large-scale networks[J]. Physical Review E, 2007, 76(3): 036106.

DOI: 10.1103/physreve.76.036106

[16] Jin D, Liu D, Yang B, Liu J (2009) fast complex network clustering algorithm using agents. Proceedings of the 8th international conference on dependable, autonomic and secure computing. 615-619.

DOI: 10.1109/dasc.2009.91

[17] Watts DJ, Strogatz SH. Collective dynamics of small-world, networks. Nature. June 1998, 393 (6684): 440–442.

DOI: 10.1038/30918

[18] Barabási A L, Albert R. Emergence of scaling in random networks[J]. science, 1999, 286(5439): 509-512.

DOI: 10.1126/science.286.5439.509