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<pdf width="1500px">https://ceur-ws.org/Vol-1398/SocInf2015_InvitedTalk1.pdf</pdf>
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Proceedings of the 1st International Workshop on Social Influence Analysis (SocInf 2015)
July 27th, 2015 - Buenos Aires, Argentina




   Invited Talk
   Computational Social Influence
   Prof. Wei Chen
   Senior Researcher at Microsoft Research Asia


   Abstract
   Social influence is deeply weaved into the fabric of human
   society and affects every aspect of human life.
   Computational social influence is aimed at empowering
   social influence with computational tools such as modeling,
   algorithm design, and data mining, so as to enable
   influence-based applications such as viral marketing,
   cascade detection, etc. In this talk, I will focus on the study
   of influence diffusion dynamics and the influence
   maximization problem, which is the problem of selecting a
   small number of seed nodes in a social network such that
   their influence coverage after the influence diffusion process
   is maximized. I will first survey recent developments in
   influence maximization including scalable influence maximization and competitive influence
   maximization, and then introduce as an example our latest work on amphibious influence
   maximization, which aims at combining traditional marketing with viral marketing and
   addresses the technical issue of how to deal with non-submodular cases in influence
   maximization. I will conclude the talk with some discussions on future directions in
   computational social influence.


   Biographical Sketch
   Wei Chen is a Senior Researcher at Microsoft Research Asia, Beijing, China. He is also an
   Adjunct Professor at Tsinghua University and a Guest Researcher at the Institute of Computing
   Technology, Chinese Academy of Sciences. His research interests include social and
   information networks, networked game theory and economics, online learning, distributed
   computing, and fault tolerance. He, together with his colleagues, has initiated the study of
   scalable influence maximization, which has been widely cited and followed upon, and the
   proposed algorithms such as PMIA, LDAG, and IRIE have been widely used as the state-of-
   the-art in influence maximization research. He has also done a series of work on modeling
   complex influence diffusion dynamics (such as competitive diffusion) and their optimization
   tasks. He has coauthored a monograph “Information and Influence Propagation in Social
   Networks” in 2013, which systematically summarizes recent advances in computational social
   influence research.

   Wei Chen won the prestigious William C. Carter Award in 2000 in the area of dependable
   computing, for his seminal dissertation work on the quality of service of failure detectors. His
   co-authored paper on a novel game-theoretic approach for community detection in social
   networks won the best student paper award in ECML PKDD 2009. He regularly serves as
   program committee members in top international data mining and data management
   conferences, such as KDD, WSDM, SDM, SIGMOD, ICDE, WWW, etc. He is a member of the
   Big Data Task Force of Chinese Computer Federation. Wei Chen obtained a Bachelor and
   Master degree from Department of Computer Science and Technology, Tsinghua University,
   and a Ph.D. from Department of Computer Science, Cornell University in 2000.

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