Synopsis
Online social networks have already become a bridge connecting our physical daily life with the (web-based) information space. This connection produces a huge volume of data, not only about the information itself, but also about user behavior. The ubiquity of the social Web and the wealth of social data offer us unprecedented opportunities for studying the interaction patterns among users so as to understand the dynamic mechanisms underlying different networks, something that was previously difficult to explore due to the lack of available data. In this book, we present the architecture of the research for social network mining, from a microscopic point of view. We focus on investigating several key issues in social networks. Specifically, we begin with analytics of social interactions between users. The first kinds of questions we try to answer are: What are the fundamental factors that form the different categories of social ties? How have reciprocal relationships been developed from parasocial relationships? How do connected users further form groups? Another theme addressed in this book is the study of social influence. Social influence occurs when one's opinions, emotions, or behaviors are affected by others, intentionally or unintentionally. Considerable research has been conducted to verify the existence of social influence in various networks. However, few literature studies address how to quantify the strength of influence between users from different aspects. In Chapter 4 and in [138], we have studied how to model and predict user behaviors. One fundamental problem is distinguishing the effects of different social factors such as social influence, homophily, and individual's characteristics. We introduce a probabilistic model to address this problem. Finally, we use an academic social network, ArnetMiner, as an example to demonstrate how we apply the introduced technologies for mining real social networks. In this system, we try to mine knowledge from both the informative (publication) network and the social (collaboration) network, and to understand the interaction mechanisms between the two networks. The system has been in operation since 2006 and has already attracted millions of users from more than 220 countries/regions.
About the Author
Jie Tang is an associate professor with the Department of Computer Science and Technology, at Tsinghua University. His interests include social network analysis, data mining, and machine learning. He has published more than 100 journal/conference papers and holds 10 patents. He served as PC Co-Chair of WSDM'15, ASONAM'15, ADMA'11, SocInfo'12, KDD-CUP Co-Chair of KDD'15, Poster Co-Chair of KDD'14, Workshop Co-Chair of KDD'13, Local Chair of KDD'12, Publication Co-Chair of KDD'11, 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 Newton Advanced Scholarship Award, CCF Young Scientist Award, NSFC Excellent Young Scholar, and IBM Innovation Faculty Award. Juanzi Li obtained her Ph.D. degree from Tsinghua University. She is now a full professor at Tsinghua University. Her main research interest is to study the semantic technologies by combining the Natural Language Processing, Semantic Web and Data Mining. She is the vice director of Chinese Information Processing Society of Chinese Computer Federation in China. She is the principal investigators of many key projects supported by the Natural Science Foundation of China (NSFC), the national basic science research program and international cooperation projects. She has published over 120 papers in many international journals and conferences such as TKDE, SIGIR, SIGMOD, SIGKDD, IJCAI, etc.
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