Social influence is the behavioral change of a person because of the perceived relationship with other people, organizations and society in general. Social influence has been a widely accepted phenomenon in social networks for decades. Many applications have been built based around the implicit notation of social influence between people, such as marketing, advertisement and recommendations. With the exponential growth of online social network services such as Facebook and Twitter, social influence can for the first time be measured over a large population. In this tutorial, we aim to provide a comprehensive review of models and algorithms on social influence and information diffusion. We first give basic definition of social influence, and introduce how to learn influence probability in large-scale social networks efficiently. Next, we present recent developments of influence and diffusion models. After that, we introduce how social influence benefits other research topics like user emotion modeling and representation learning for social networks. We finally demonstrate how to apply the presented methodologies in real social networks, giving Tencent Game the examples. The tutorial will be held at the 26th International Joint Conference on Artificial Intelligence in Melbourne, Australia on August 19, 2017.
Yang Yang is an assistant professor of College of Computer Science and Technology, Zhejiang University. His research focuses on mining knowledge from large-scale social and information networks, with an emphasis on studying information diffusion process. He has published a number of papers at top conferences and journals including KDD, AAAI, TOIS, TKDD. He has served as program committee member of WWW'17, WSDM'17‘16, ICWSM’17, CIKM'17’16, WSDM’16, and ASONAM’15.
Jie Tang is an associate professor with Department of Computer Science and Technology, Tsinghua University. His interests include social network analysis, data mining, and machine learning. He published more than 100 journal/conference papers and holds 10 patents. He served as PC Co-Chair of WSDM15, ASONAM15, ADMA11, SocInfo12, KDD-CUP Co-Chair of KDD15, Poster Co-Chair of KDD14,Workshop Co-Chair of KDD13, Local Chair of KDD12, Publication Co-Chair of KDD11, and as the PC member of more than 50 international conferences. He is the principal investigator of National High-tech R&D Program (863), NSFC project, Chinese Young Faculty Research Funding, National 985 funding, and international collaborative projects with Minnesota University, IBM, Google, Nokia, Sogou, etc. He leads the project ArnetMiner.org for academic social network analysis and mining, which has attracted millions of independent IP accesses from 220 countries/regions in the world. He was honored with the CCF Young Scientist Award, NSFC Excellent Young Scholar, and IBM Innovation Faculty Award.
This tutorial targets researchers and professionals with interests in social network analysis, and practitioners from industry, as the tutorial covers many interesting topics about social network theories, human factor modeling, and their real applications.