Persuasion driven influence propagation in social networks

With the explosive growth of online social network services such as Facebook and Twitter, people now are providing ample opportunities to share information, ideas, and innovations among others. Studying social influence and information diffusion in online social networks can be extremely useful for various real-life applications, notably influencer marketing and viral marketing. Influence maximization problem, motivated by viral marketing applications, has received extensive attentions from social network analysis community in recent years. The goal of the problem is to find a small set of k seed nodes in a social network that maximize the influence spread under certain influence propagation models. Based on the widely adopted Independent Cascade model, a Persuasiveness Aware Cascade (PAC) model which considers social persuasion in influence propagation is proposed. In this model, the user-to-user influence probability is estimated by three types of social persuasion, namely, tie strength, peer conformity, and authority. Experiments conducted over real-world social networks suggest that the proposed model with the new social persuasion measures is more effective in describing real-world influence propagation than the well-studied propagation models for influence maximization.

[1]  A-L Barabási,et al.  Structure and tie strengths in mobile communication networks , 2006, Proceedings of the National Academy of Sciences.

[2]  Ning Zhang,et al.  Time-Critical Influence Maximization in Social Networks with Time-Delayed Diffusion Process , 2012, AAAI.

[3]  Jure Leskovec,et al.  Information diffusion and external influence in networks , 2012, KDD.

[4]  Nicola Barbieri,et al.  Topic-aware social influence propagation models , 2012, Knowledge and Information Systems.

[5]  Laks V. S. Lakshmanan,et al.  Learning influence probabilities in social networks , 2010, WSDM '10.

[6]  Qian Zhang,et al.  Analyzing the influential people in Sina Weibo dataset , 2013, 2013 IEEE Global Communications Conference (GLOBECOM).

[7]  Laks V. S. Lakshmanan,et al.  CELF++: optimizing the greedy algorithm for influence maximization in social networks , 2011, WWW.

[8]  Lada A. Adamic,et al.  The role of social networks in information diffusion , 2012, WWW.

[9]  John S. Seiter,et al.  Persuasion: Social Inflence and Compliance Gaining , 2015 .

[10]  Dong Xu,et al.  Time Constrained Influence Maximization in Social Networks , 2012, 2012 IEEE 12th International Conference on Data Mining.

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

[12]  Ambuj K. Singh,et al.  The social media genome: Modeling individual topic-specific behavior in social media , 2013, 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013).

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

[14]  Terry Hui-Ye Chiu,et al.  Propagating online social networks via different kinds of weak ties , 2013, 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013).

[15]  Mark S. Granovetter.  T H E S T R E N G T H O F WEAK TIES: A NETWORK THEORY REVISITED , 1983 .

[16]  Guandong Xu.  2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2014, Beijing, China, August 17-20, 2014 , 2014 .

[17]  Wei Chen,et al.  Scalable influence maximization for prevalent viral marketing in large-scale social networks , 2010, KDD.

[18]  R. Cialdini.  Influence: Science and Practice , 1984 .

[19]  Wei Chen,et al.  Efficient influence maximization in social networks , 2009, KDD.

[20]  Sara B. Kiesler,et al.  The Equalization Phenomenon: Status Effects in Computer-Mediated and Face-to-Face Decision-Making Groups , 1991, Hum. Comput. Interact..

[21]  Masahiro Kimura,et al.  Prediction of Information Diffusion Probabilities for Independent Cascade Model , 2008, KES.

[22]  Sangkeun Lee,et al.  Influence Maximization Algorithm Using Markov Clustering , 2013, DASFAA Workshops.

[23]  Matthew Richardson,et al.  Mining the network value of customers , 2001, KDD '01.

[24]  Jimeng Sun,et al.  Confluence: conformity influence in large social networks , 2013, KDD.

[25]  Qi He,et al.  TwitterRank: finding topic-sensitive influential twitterers , 2010, WSDM '10.

[26]  Andreas Krause,et al.  Cost-effective outbreak detection in networks , 2007, KDD '07.