Identifying Influencers in Social Networks

The central idea in designing various marketing strategies for online social networks is to identify the influencers in the network. The influential individuals induce “word-of-mouth” effects in the network. These individuals are responsible for triggering long cascades of influence that convince their peers to perform a similar action (buying a product, for instance). Targeting these influentials usually leads to a vast spread of the information across the network. Hence it is important to identify such individuals in a network. One way to measure an individual’s influencing capability on its peers is by its reach for a certain action. We formulate identifying the influencers in a network as a problem of predicting the average depth of cascades an individual can trigger. We first empirically identify factors that play crucial role in triggering long cascades. Based on the analysis, we build a model for predicting the cascades triggered by a user for an action. The model uses features like influencing capabilities of the user and their friends, influencing capabilities of the particular action and other user and network characteristics. Experiments show that the model effectively improves the predictions over several baselines.

[1]  Jimeng Sun,et al.  Social influence analysis in large-scale networks , 2009, KDD.

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

[3]  Jennifer Neville,et al.  Randomization tests for distinguishing social influence and homophily effects , 2010, WWW '10.

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

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

[6]  Ravi Kumar,et al.  Influence and correlation in social networks , 2008, KDD.

[7]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

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

[9]  Rajesh Parekh,et al.  Predicting product adoption in large-scale social networks , 2010, CIKM.

[10]  Arun Sundararajan,et al.  Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks , 2009, Proceedings of the National Academy of Sciences.

[11]  Dylan Walker,et al.  Creating Social Contagion Through Viral Product Design: A Randomized Trial of Peer Influence in Networks , 2010, ICIS.

[12]  Vahab S. Mirrokni,et al.  Optimal marketing strategies over social networks , 2008, WWW.

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

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

[15]  Matthew Richardson,et al.  Yes, there is a correlation: - from social networks to personal behavior on the web , 2008, WWW.

[16]  Krishna P. Gummadi,et al.  Characterizing social cascades in flickr , 2008, WOSN '08.

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

[18]  D. Watts,et al.  Influentials, Networks, and Public Opinion Formation , 2007 .