Uncharted but not Uninfluenced: Influence Maximization with an Uncertain Network

This paper focuses on new challenges in influence maximization inspired by non-profits' use of social networks to effect behavioral change in their target populations. Influence maximization is a multiagent problem where the challenge is to select the most influential agents from a population connected by a social network. Specifically, our work is motivated by the problem of spreading messages about HIV prevention among homeless youth using their social network. We show how to compute solutions which are provably close to optimal when the parameters of the influence process are unknown. We then extend our algorithm to a dynamic setting where information about the network is revealed at each stage. Simulation experiments using real world networks collected by the homeless shelter show the advantages of our approach.

[1]  David Kempe,et al.  Robust Influence Maximization , 2016, KDD.

[2]  Lior Seeman,et al.  Adaptive Seeding in Social Networks , 2013, 2013 IEEE 54th Annual Symposium on Foundations of Computer Science.

[3]  N. Christakis,et al.  Social network targeting to maximise population behaviour change: a cluster randomised controlled trial , 2015, The Lancet.

[4]  Mohammad R. Salavatipour,et al.  Packing Steiner trees , 2003, SODA '03.

[5]  Wei Chen,et al.  Robust Influence Maximization , 2016, KDD.

[6]  Xiaokui Xiao,et al.  Influence maximization: near-optimal time complexity meets practical efficiency , 2014, SIGMOD Conference.

[7]  Haifeng Xu,et al.  Using Social Networks to Aid Homeless Shelters: Dynamic Influence Maximization under Uncertainty , 2016, AAMAS.

[8]  Yang Cai,et al.  Reducing Revenue to Welfare Maximization: Approximation Algorithms and other Generalizations , 2013, SODA.

[9]  Milind Tambe,et al.  Influence Maximization in the Field: The Arduous Journey from Emerging to Deployed Application , 2017, AAMAS.

[10]  Avrim Blum,et al.  Planning in the Presence of Cost Functions Controlled by an Adversary , 2003, ICML.

[11]  N. Milburn,et al.  Mobilizing homeless youth for HIV prevention: a social network analysis of the acceptability of a face-to-face and online social networking intervention. , 2012, Health education research.

[12]  Laks V. S. Lakshmanan,et al.  A Data-Based Approach to Social Influence Maximization , 2011, Proc. VLDB Endow..

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

[14]  Doug McAdam,et al.  Specifying the Relationship Between Social Ties and Activism , 1993, American Journal of Sociology.

[15]  Peter H. Reingen,et al.  Social Ties and Word-of-Mouth Referral Behavior , 1987 .

[16]  E. Rice.  The Positive Role of Social Networks and Social Networking Technology in the Condom-Using Behaviors of Homeless Young People , 2010, Public health reports.

[17]  Leandro Soriano Marcolino,et al.  Preventing HIV Spread in Homeless Populations Using PSINET , 2015, AAAI.

[18]  Camille Roth,et al.  How Realistic Should Knowledge Diffusion Models Be? , 2007, J. Artif. Soc. Soc. Simul..

[19]  Jure Leskovec,et al.  Inferring networks of diffusion and influence , 2010, KDD.

[20]  Sujay Sanghavi,et al.  Learning the graph of epidemic cascades , 2012, SIGMETRICS '12.

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

[22]  R. Campbell,et al.  Identifying influential young people to undertake effective peer-led health promotion: the example of A Stop Smoking In Schools Trial (ASSIST). , 2009, Health education research.

[23]  T. Valente.  Network Interventions , 2012, Science.

[24]  E. Rice,et al.  Sexuality and homelessness in Los Angeles public schools. , 2012, American journal of public health.

[25]  Laks V. S. Lakshmanan,et al.  Influence Maximization with Bandits , 2015, ArXiv.

[26]  David C. Parkes,et al.  Learnability of Influence in Networks , 2015, NIPS.