Analysis of the Evolution of the Influence of Central Nodes in a Twitter Social Network

Both practitioners and researchers are becoming increasingly interested in viral marketing. For viral marketing on social media, it is important to find influencers who can spread information to many other users. Although finding influencers on social media is a topic of much research interest in the field of network science, most previous studies ignored the time evolution of the influence of social media users. In this paper, we investigate the time evolution of the influence of Twitter users. We construct a social network of 0.3 million Twitter users, extract the central nodes in the network using five centrality measures, and investigate how the actual influence of those central nodes changes over a year. We show that the overlap between the central nodes and actual influencers did not change drastically during the one-year period, suggesting that the influence of central nodes is stable over time.

[1]  Scott Counts,et al.  Identifying topical authorities in microblogs , 2011, WSDM '11.

[2]  Jon Kleinberg,et al.  Maximizing the spread of influence through a social network , 2003, KDD '03.

[3]  Stephen B. Seidman,et al.  Network structure and minimum degree , 1983 .

[4]  Samee Ullah Khan,et al.  Analysis of Online Social Network Connections for Identification of Influential Users , 2018, ACM Comput. Surv..

[5]  Sho Tsugawa,et al.  Identifying influencers from sampled social networks , 2018, Physica A: Statistical Mechanics and its Applications.

[6]  Matthew Richardson,et al.  Mining knowledge-sharing sites for viral marketing , 2002, KDD.

[7]  Jafar Adibi,et al.  Discovering important nodes through graph entropy the case of Enron email database , 2005, LinkKDD '05.

[8]  Duncan J. Watts,et al.  Everyone's an influencer: quantifying influence on twitter , 2011, WSDM '11.

[9]  Sho Tsugawa,et al.  A Survey of Social Network Analysis Techniques and their Applications to Socially Aware Networking , 2018, IEICE Trans. Commun..

[10]  Stephen P. Borgatti,et al.  Identifying sets of key players in a social network , 2006, Comput. Math. Organ. Theory.

[11]  Fang Wu,et al.  Social Networks that Matter: Twitter Under the Microscope , 2008, First Monday.

[12]  Éva Tardos,et al.  Maximizing the Spread of Influence through a Social Network , 2015, Theory Comput..

[13]  Zhiming Zheng,et al.  Searching for superspreaders of information in real-world social media , 2014, Scientific Reports.

[14]  L. Freeman.  Centrality in social networks conceptual clarification , 1978 .

[15]  Sergey Brin,et al.  The Anatomy of a Large-Scale Hypertextual Web Search Engine , 1998, Comput. Networks.

[16]  Duanbing Chen,et al.  Vital nodes identification in complex networks , 2016, ArXiv.

[17]  Timothy W. Finin,et al.  Why we twitter: understanding microblogging usage and communities , 2007, WebKDD/SNA-KDD '07.

[18]  Krishna P. Gummadi,et al.  Measuring User Influence in Twitter: The Million Follower Fallacy , 2010, ICWSM.