SAE: social analytic engine for large networks

Authors:
Yang Yang;Jianfei Wang;Yutao Zhang;Wei Chen;Jing Zhang;Honglei Zhuang;Zhilin Yang;Bo Ma;Zhanpeng Fang;Sen Wu;Xiaoxiao Li;Debing Liu;Jie Tang
Affiliations:
Tsinghua University, Beijing, China;Tsinghua University, Beijing, China;Tsinghua University, Beijing, China;Tsinghua University, Beijing, China;Tsinghua University, Beijing, China;Tsinghua University, Beijing, China;Tsinghua University, Beijing, China;Tsinghua University, Beijing, China;Tsinghua University, Beijing, China;Tsinghua University, Beijing, China;Tsinghua University, Beijing, China;Tsinghua University, Beijing, China;Tsinghua University, Beijing, China
Venue:
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Year:
2013

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Abstract

Online social networks become a bridge to connect our physical daily life and the virtual Web space, which not only provides rich data for mining, but also brings many new challenges. In this paper, we present a novel Social Analytic Engine (SAE) for large online social networks. The key issues we pursue in the analytic engine are concerned with the following problems: 1) at the micro-level, how do people form different types of social ties and how people influence each other? 2) at the meso-level, how do people group into communities? 3) at the macro-level, what are the hottest topics in a social network and how the topics evolve over time? We propose methods to address the above questions. The methods are general and can be applied to various social networking data. We have deployed and validated the proposed analytic engine over multiple different networks and validated the effectiveness and efficiency of the proposed methods.