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comeNgo: A Dynamic Model for Social Group Evolution

Published:27 July 2017Publication History
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Abstract

How do social groups, such as Facebook groups and Wechat groups, dynamically evolve over time? How do people join the social groups, uniformly or with burst? What is the pattern of people quitting from groups? Is there a simple universal model to depict the come-and-go patterns of various groups?

In this article, we examine temporal evolution patterns of more than 100 thousands social groups with more than 10 million users. We surprisingly find that the evolution patterns of real social groups goes far beyond the classic dynamic models like SI and SIR. For example, we observe both diffusion and non-diffusion mechanism in the group joining process, and power-law decay in group quitting process, rather than exponential decay as expected in SIR model. Therefore, we propose a new model comeNgo, a concise yet flexible dynamic model for group evolution. Our model has the following advantages: (a) Unification power: it generalizes earlier theoretical models and different joining and quitting mechanisms we find from observation. (b) Succinctness and interpretability: it contains only six parameters with clear physical meanings. (c) Accuracy: it can capture various kinds of group evolution patterns preciously, and the goodness of fit increases by 58% over baseline. (d) Usefulness: it can be used in multiple application scenarios, such as forecasting and pattern discovery. Furthermore, our model can provide insights about different evolution patterns of social groups, and we also find that group structure and its evolution has notable relations with temporal patterns of group evolution.

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    • Published in

      cover image ACM Transactions on Knowledge Discovery from Data
      ACM Transactions on Knowledge Discovery from Data  Volume 11, Issue 4
      Special Issue on KDD 2016 and Regular Papers
      November 2017
      419 pages
      ISSN:1556-4681
      EISSN:1556-472X
      DOI:10.1145/3119906
      • Editor:
      • Jie Tang
      Issue’s Table of Contents

      Copyright © 2017 ACM

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      Publication History

      • Published: 27 July 2017
      • Revised: 1 March 2017
      • Accepted: 1 March 2017
      • Received: 1 November 2016
      Published in tkdd Volume 11, Issue 4

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