Identification of Key Nodes in Microblog Networks

  • Lu, Jing (Department of Communication and Information Engineering, Shanghai University, school of Electronics and Information Engineering, Shanghai University of Electric Power) ;
  • Wan, Wanggen (Department of Communication and Information Engineering, Shanghai University)
  • Received : 2015.08.14
  • Accepted : 2015.10.14
  • Published : 2016.02.01


A microblog is a service typically offered by online social networks, such as Twitter and Facebook. From the perspective of information dissemination, we define the concept behind a spreading matrix. A new WeiboRank algorithm for identification of key nodes in microblog networks is proposed, taking into account parameters such as a user's direct appeal, a user's influence region, and a user's global influence power. To investigate how measures for ranking influential users in a network correlate, we compare the relative influence ranks of the top 20 microblog users of a university network. The proposed algorithm is compared with other algorithms - PageRank, Betweeness Centrality, Closeness Centrality, Out-degree - using a new tweets propagation model - the Ignorants-Spreaders-Rejecters model. Comparison results show that key nodes obtained from the WeiboRank algorithm have a wider transmission range and better influence.


Supported by : National Nature Science Foundation of China


  1. H. Kwak et al., "What is Twitter, a Social Network or a News Media?" Int. Conf. World Wide Web, Raleigh, NC, USA, Apr. 26-30, 2010, pp. 591-600.
  2. M. Cha et al., "Measuring User Influence in Twitter: The Million Follower Fallacy," Int. AAAI Conf. Weblogs Soc. Media, Washington, DC, USA, May 23-26, 2010, pp. 10-17.
  3. J. Sun and J. Tang, "Models and Algorithms for Social Influence Analysis," ACM Int. Conf. Web Search Data Mining, Rome, Italy, Feb. 4-8, 2013, pp. 775-776.
  4. J. Zhang et al., "Social Influence Locality for Modeling Retweeting Behaviors," Int. Joint Conf. Artif. Intell., Beijing, China, Aug. 3-9, 2013, pp. 2761-2767.
  5. R. Walisa and P. Wichian, "Applying Mining Fuzzy Sequential Patterns Technique to Predict the Leadership in Social Networks," Int. Conf. ICT Knowl. Eng., Bangkok, Thailand, Jan. 12-13, 2012, pp. 134-137.
  6. K. Song et al., "Detecting Opinion Leader Dynamically in Chinese News Comments," Lecture Notes Comput. Sci., Wuhan, China, Sept. 14-16, 2011, pp. 197-209.
  7. B. Freimut and K. Carolin, "Detecting Opinion Leaders and Trends in Online Communities," Int. Conf. Digit. Soc., St. Maarten, Netherlands, Feb. 10-16, 2010, pp. 124-129.
  8. Y. Cho, J. Hwang, and D. Lee, "Identification of Effective Opinion Leaders in the Diffusion of Technological Innovation: A Social Network Approach," Technol. Forecasting Soc. Change, vol. 79, no. 1, Jan. 2012, pp. 97-106.
  9. F. Li and T.C. Du, "Who is Talking? An Ontology-Based Opinion Leader Identification Framework for Word-of-Mouth Marketing in Online Social Blogs," Decision Support Syst., vol. 51, no. 1, Apr. 2011, pp. 190-197.
  10. M. Kitsak et al., "Identification of Influential Spreaders in Complex Networks," Nature Physics, vol. 6, no. 11, Aug. 2010, pp. 888-893.
  11. J.-G. Liu, Z.-M. Ren, and Q. Guo, "Ranking the Spreading Influence in Complex Networks," Physica A: Statistical Mechanics Appl., vol. 392, no. 18, Sept. 2013, pp. 4154-4159.
  12. C. Kang et al., "Diffusion Centrality in Social Networks," IEEE/ACM Int. Conf. Adv. Soc. Netw. Anal. Mining, Istanbul, Turkey, Aug. 26-29, 2012, pp. 558-564.
  13. S. Gao et al., "Ranking the Spreading Ability of Nodes in Complex Networks Based on Local Structure," Physica A: Statistical Mechanics Appl., vol. 403, June 2014, pp. 130-147.
  14. N.E. Friedkin, "Theoretical Foundations for Centrality Measures," American J. Sociology, vol. 96, no. 6, May 1991, pp. 1478-1504.
  15. S.P. Borgatti, "Centrality and Network Flow," Soc. Netw., vol. 27, no. 1, Jan. 2005, pp. 55-71.
  16. A. Mislove et al., "Measurement and Analysis of Online Social Networks," ACM SIGCOMM Internet Meas. Conf., San Diego, CA, USA, Oct. 24-26, 2007, pp. 29-42.
  17. V. Kandiah and D.L. Shepelyansky, "PageRank Model of Opinion Formation on Social Networks," Physica A: Statistical Mechanics Appl., vol. 391, no. 22, Nov. 2012, pp. 5779-5793.
  18. M.G. Kendall, "A New Measure of Rank Correlation," Biometrika, vol. 30, no. 1-2, June 1938, pp. 81-93.