Predictive Modeling of information Spreading in Social Networks
报告题目:Predictive Modeling of information Spreading in Social Networks
In a network environment, if decentralized nodes act on the basis of how their neighbors act at earlier time, these local actions often lead to interesting macro dynamics - cascades. There has been a growing body of research on these information cascades because of their big potential in various vital applications such as viral marketing, epidemic prevention, and traffic management, but how to predict the dynamic process of information spreading is still an open problem. In this talk, I will present some of our recent works on predictive modeling on information cascades, including one-hop cascade prediction, cascading outbreak prediction, as well as cascading process prediction.
Peng Cui is now an Assistant Professor in Tsinghua University, China. He received his PhD degree from Tsinghua University in 2010. He is an active researcher dedicated to novel algorithms and systems in social network analysis and social multimedia computing, and he is keen to promote the convergence of social media data mining and multimedia computing technologies. Dr. Cui has strong backgrounds in both data mining and multimedia communities. He has published more than 50 papers in prestigious conferences and journals in data mining and multimedia, including ACM MM, SIGKDD, SIGIR, AAAI, IEEE TMM, IEEE TKDE, IEEE TIP etc. His recent research won the ICDM 2015 Best Student Paper Award, SIGKDD 2014 Best Paper Finalist, IEEE ICME 2014 Best Paper Award, ACM MM12 Grand Challenge Multimodal Award, and MMM13 Best Paper Award. He is the Area Chair of ICDM 2016, ACM MM2014-2015, IEEE ICME 2014-2015, ICASSP 2013, Associate Editor of ACM TOMM, Elsivier Journal on Neurocomputing, Frontier of Computer Science journal, Guest Editor of IEEE Intelligent Systems, Information Retrieval journal, and co-organized several special sessions and workshops in ICMR, ICME, ACM MM and WSDM.
活动宣传：Dr.Peng Cui作《Predictive Modeling of information Spreading in Social Networks》报告