Communicating in a socially-aware network: Impact of relationship types

Communication networks are linked to and influenced by human interactions. Socially-aware systems should integrate these complex relationship patterns in the network design. This paper studies the impact of friendship and antagonistic relationships between individuals on optimal network propagation policies. We develop a network propagation model for signed networks, and determine the optimal policies to influence a target node with an opinion while minimizing the total number of persons against it. We also provide extensions to this problem to elaborate on the impact of network parameters, such as minimum-delay propagation, while limiting the number of persons influenced against the idea before reaching the target. We provide numerical evaluations in a synthetic setup as well as the Epinions online social dataset. We demonstrate that propagation schemes with social and influence-centric constraints should take into account the relationship types in network design.

[1]  Masahiro Kimura,et al.  Tractable Models for Information Diffusion in Social Networks , 2006, PKDD.

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

[3]  Tad Hogg,et al.  Friends and foes: ideological social networking , 2008, CHI.

[4]  Jure Leskovec,et al.  Signed networks in social media , 2010, CHI.

[5]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[6]  Chris Arney,et al.  Networks, Crowds, and Markets: Reasoning about a Highly Connected World (Easley, D. and Kleinberg, J.; 2010) [Book Review] , 2013, IEEE Technology and Society Magazine.

[7]  Yifei Yuan,et al.  Scalable Influence Maximization in Social Networks under the Linear Threshold Model , 2010, 2010 IEEE International Conference on Data Mining.

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

[9]  Paul Resnick,et al.  Follow the reader: filtering comments on slashdot , 2007, CHI.

[10]  Tad Hogg,et al.  Multiple Relationship Types in Online Communities and Social Networks , 2008, AAAI Spring Symposium: Social Information Processing.

[11]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

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

[13]  Wei Chen,et al.  Influence diffusion dynamics and influence maximization in social networks with friend and foe relationships , 2011, WSDM.

[14]  Christian Bauckhage,et al.  The slashdot zoo: mining a social network with negative edges , 2009, WWW.

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

[16]  P. Brown,et al.  European Alliances and Alignments, 1871-1890. , 1932, American Journal of International Law.

[17]  Aylin Yener,et al.  Optimal strategies for targeted influence in signed networks , 2014, 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014).

[18]  B. E. Schmitt,et al.  Triple alliance and triple entente , 1934 .

[19]  M. McPherson,et al.  Birds of a Feather: Homophily in Social Networks , 2001 .

[20]  Malik Magdon-Ismail,et al.  Communities and Balance in Signed Networks: A Spectral Approach , 2012, 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining.

[21]  E. David,et al.  Networks, Crowds, and Markets: Reasoning about a Highly Connected World , 2010 .

[22]  B. E. Schmitt.  Triple Alliance and Triple Entente, 1902–1914 , 1924 .