Adaptive Seeding in Social Networks

The algorithmic challenge of maximizing information diffusion through word-of-mouth processes in social networks has been heavily studied in the past decade. While there has been immense progress and an impressive arsenal of techniques has been developed, the algorithmic frameworks make idealized assumptions regarding access to the network that can often result in poor performance of state-of-the-art techniques. In this paper we introduce a new framework which we call Adaptive Seeding. The framework is a two-stage stochastic optimization model designed to leverage the potential that typically lies in neighboring nodes of arbitrary samples of social networks. Our main result is an algorithm which provides a constant factor approximation to the optimal adaptive policy for any influence function in the Triggering model.

[1]  Christian Borgs,et al.  Influence Maximization in Social Networks: Towards an Optimal Algorithmic Solution , 2012, ArXiv.

[2]  L. Wolsey.  Maximising Real-Valued Submodular Functions: Primal and Dual Heuristics for Location Problems , 1982, Math. Oper. Res..

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

[4]  Jure Leskovec,et al.  The dynamics of viral marketing , 2005, EC '06.

[5]  R. Ravi,et al.  Approximation algorithms for stochastic orienteering , 2012, SODA.

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

[7]  Yuval Rabani,et al.  Allocating bandwidth for bursty connections , 1997, STOC '97.

[8]  Ning Chen,et al.  On the approximability of influence in social networks , 2008, SODA '08.

[9]  R. Ravi,et al.  Boosted sampling: approximation algorithms for stochastic optimization , 2004, STOC '04.

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

[11]  Silvio Lattanzi,et al.  The Power of Random Neighbors in Social Networks , 2015, WSDM.

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

[13]  Andreas Krause,et al.  Adaptive Submodularity: Theory and Applications in Active Learning and Stochastic Optimization , 2010, J. Artif. Intell. Res..

[14]  Chaitanya Swamy,et al.  Stochastic optimization is (almost) as easy as deterministic optimization , 2004, 45th Annual IEEE Symposium on Foundations of Computer Science.

[15]  S. Feld.  Why Your Friends Have More Friends Than You Do , 1991, American Journal of Sociology.

[16]  Alexander Shapiro,et al.  Lectures on Stochastic Programming: Modeling and Theory , 2009 .

[17]  Aristides Gionis,et al.  Sparsification of influence networks , 2011, KDD.

[18]  M. L. Fisher,et al.  An analysis of approximations for maximizing submodular set functions—I , 1978, Math. Program..

[19]  Alexander Shapiro,et al.  The Sample Average Approximation Method for Stochastic Discrete Optimization , 2002, SIAM J. Optim..

[20]  Lars Backstrom,et al.  The Anatomy of the Facebook Social Graph , 2011, ArXiv.

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

[22]  R. Holley,et al.  Ergodic Theorems for Weakly Interacting Infinite Systems and the Voter Model , 1975 .

[23]  Anna R. Karlin,et al.  Beyond VCG: frugality of truthful mechanisms , 2005, 46th Annual IEEE Symposium on Foundations of Computer Science (FOCS'05).

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

[25]  Nicole Immorlica,et al.  On the costs and benefits of procrastination: approximation algorithms for stochastic combinatorial optimization problems , 2004, SODA '04.

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

[27]  Amin Saberi,et al.  Stochastic Submodular Maximization , 2008, WINE.

[28]  Nima Haghpanah,et al.  Optimal Auctions with Positive Network Externalities , 2013, TEAC.

[29]  Elchanan Mossel,et al.  Submodularity of Influence in Social Networks: From Local to Global , 2010, SIAM J. Comput..

[30]  Chaitanya Swamy,et al.  An approximation scheme for stochastic linear programming and its application to stochastic integer programs , 2006, JACM.

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

[32]  Aravind Srinivasan,et al.  Approximation algorithms for stochastic and risk-averse optimization , 2007, SODA '07.

[33]  J. Vondrák,et al.  Approximating the Stochastic Knapsack Problem: The Benefit of Adaptivity , 2008 .

[34]  R. Ravi,et al.  Hedging Uncertainty: Approximation Algorithms for Stochastic Optimization Problems , 2004, Math. Program..

[35]  Eyal Even-Dar,et al.  A note on maximizing the spread of influence in social networks , 2007, Inf. Process. Lett..