Utilizing statistical physics and machine learning to discover collective behavior on temporal social networks |
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| Affiliation: | 1. Department of Management Science and Engineering, School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China;2. School of Management and Engineering, Capital University of Economics and Business, Beijing 100070, China;1. McCormick School of Engineering, Northwestern University, 633 Clark St, Evanston, IL 60208, United States;2. Division of Social Sciences, Duke Kunshan University, Division of Social Sciences, Duke Kunshan University, No. 8 Duke Avenue, Kunshan, Jiangsu 215316, China;3. Graduate Institute Geneva, Chem. Eugène-Rigot 2, Genève 1202, Switzerland;4. Division of Natural Sciences, Duke Kunshan University, Division of Social Sciences, Duke Kunshan University, No. 8 Duke Avenue, Kunshan, Jiangsu 215316, China;1. Post-Doctoral Research Center, China Central Depository & Clearing Co., Ltd., Beijing, China;2. School of Software, Tsinghua University, Beijing, China;3. School of Information, Renmin University of China, Beijing, China |
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| Abstract: | Computational social science has become a branch of social science that uses computationally intensive ways to investigate and model social phenomena. Exploitation on mathematics, physics, and computer sciences, and analytic approaches like Social Network Analysis (SNA), Machine Learning (ML), etc, develops and tests the theories of complex social phenomena. In the emerging environment of social media, the new characteristics of social collective behavior and its extensive phenomena have become the hot spot of common concern across many disciplines. In this paper, we propose a general quantitative framework to discover the social collective behavior in temporal social networks. The general framework incorporates the Time-Correlation Function (T.C.F.) in statistical physics and evolutionary approach in Machine Learning, and provides the quantitative evidence of the existence of social collective behavior. Results show collective behaviors are observed and there exists a tiny fraction of users whose behavior are constantly replicated by public, disregard of the behavior itself. Our method is assumption-independent and has the potential to be applied to various temporal systems. |
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| Keywords: | Social collective behavior Evolutionary machine learning Social networks Time-correlation-functions |
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