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Issue No.09 - September (2013 vol.46)
pp: 78-84
Jianwei Niu , Beihang University
Jing Peng , Beihang University
Lei Shu , Guangdong University of Petrochemical Technology
Chao Tong , Beihang University
Wanjiun Liao , National Taiwan University
Deeper knowledge of social networks' structure and temporal evolution enhances data mining for both research and education purposes. An empirical analysis of a Chinese social network, Renren, shows that it follows an exponentially truncated power law in degree distribution, and has a short average node distance.
Social network services, Communities, Data mining, Facebook, China, Electronic mail,data mining, graph mining, social networks, network structure, network evolution, Renren
Jianwei Niu, Jing Peng, Lei Shu, Chao Tong, Wanjiun Liao, "An Empirical Study of a Chinese Online Social Network--Renren", Computer, vol.46, no. 9, pp. 78-84, September 2013, doi:10.1109/MC.2013.1
1. J. Leskovec,J. Kleinberg,, and C. Faloutsos,“Graphs Over Time: Densification Laws, Shrinking Diameters and Possible Explanations,” Proc. 11th Int’l Conf. Knowledge Discovery and Data Mining (KDD 05), ACM, 2005, pp. 177-187.
2. J. Leskovec et al., “Microscopic Evolution of Social Networks,” Proc. 14th Int’l Conf. Knowledge Discovery and Data Mining (KDD 08), ACM, 2008, pp. 462-470.
3. M. Faloutsos,P. Faloutsos,, and C. Faloutsos,“On Power-Law Relationships of the Internet Topology,” ACM SIGCOMM Computer Comm. Rev., vol. 29, no. 4, 1999, pp. 251-262.
4. A. Mislove et al., “Measurement and Analysis of Online Social Networks,” Proc. 7th Int’l Conf. Internet Meas. (IMC 07), ACM, 2007, pp. 29-42.
5. M. Newman,“The Structure of Scientific Collaboration Networks,” Proc. Nat’l Academy of Sciences, vol. 98, no. 2, 2001, p. 404.
6. R. Kumar,J. Novak,, and A. Tomkins,“Structure and Evolution of Online Social Networks,” Proc. 12th Int’l Conf. Knowledge Discovery and Data Mining (KDD 06), ACM, 2006, pp. 611-617.
7. Y. Ahn et al., “Analysis of Topological Characteristics of Huge Online Social Networking Services,” Proc. 16th Int’l Conf. World Wide Web (WWW 07), ACM, 2007, pp. 835-844.
8. D. Romero and J. Kleinberg,“The Directed Closure Process in Hybrid Social-Information Networks, with an Analysis of Link Formation on Twitter,” Proc. 4th Int’l Conf. Weblogs and Social Media (ICWSM 10), AAAI, 2010, pp. 138-145.
9. M. McGlohon,L. Akoglu,, and C. Faloutsos,“Weighted Graphs and Disconnected Components: Patterns and a Generator,” Proc. 14th Int’l Conf. Knowledge Discovery and Data Mining (KDD 08), ACM, 2008, pp. 524-532.
10. Y. Jia,Y. Zhao,, and Y. Lin,“Effects of System Characteristics on Users’ Self-Disclosure in Social Networking Sites,” Proc. 7th Int’l Conf. Information Technology: New Generations (ITNG 10), IEEE, 2010, pp. 529-533.
11. J. Jiang et al., “Understanding Latent Interactions in Online Social Networks,” Proc. 10th Conf. Internet Meas. (IMC 10), ACM, 2010, pp. 369-382.
12. X. Zhao et al., “Multi-Scale Dynamics in a Massive Online Social Network,” Proc. 12th Conf. Internet Meas. (IMC 12), ACM, 2012, pp. 171-184.
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