Semantic Mining of Social Networks

Semantic Mining of Social Networks
Morgan & Claypool Publishers
Synthesis Lectures on The Semantic Web: Theory and Technology
ISBN: 9781608458578 | PDF ISBN: 9781608458585
August 2015 | 193 Pages
DOI: 10.2200/S00629ED1V01Y201502WBE011
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Jie Tang (Tsinghua University), Juanzi Li (Tsinghua University)


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. 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.

Table of Contents: Acknowledgments / Introduction / Social Tie Analysis / Social Influence Analysis / User Behavior Modeling and Prediction / ArnetMiner: Deep Mining for Academic Social Networks / Research Frontiers / Bibliography / Authors’ Biographies

Download Chapter 1.: Semantic_Mining_Ch1



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