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Predicting aggregate social activities using continuous-time stochastic process

Published: 29 October 2012 Publication History

Abstract

How to accurately model and predict the future status of social networks has become an important problem in recent years. Conventional solutions to such a problem often employ topological structure of the sociogram, i.e., friendship links. However, they often disregard different levels of activeness of social actors and become insufficient to deal with complex dynamics of user behaviors. In this paper, to address this issue, we first refine the notion of social activity to better describe dynamic user behaviors in social networks. We then propose a Parameterized Social Activity Model (PSAM) using continuous-time stochastic process for predicting aggregate social activities. With social activities evolving over time, PSAM itself also evolves and therefore dynamically captures the real-time characteristics of the current active population. Our experiments using two real social networks (Facebook and CiteSeer) reveal that the proposed PSAM model is effective in simulating social activity evolution and predicting aggregate social activities accurately at different time scales.

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cover image ACM Conferences
CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management
October 2012
2840 pages
ISBN:9781450311564
DOI:10.1145/2396761
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 29 October 2012

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  1. aggregate social activity
  2. continuous-time stochastic process

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  • (2017)Learning to share: Engineering adaptive decision-support for online social networks2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)10.1109/ASE.2017.8115641(280-285)Online publication date: Oct-2017
  • (2016)Telco User Activity Level Prediction with Massive Mobile Broadband DataACM Transactions on Intelligent Systems and Technology10.1145/28560577:4(1-30)Online publication date: 2-May-2016
  • (2016)A supervised approach for intra-/inter-community interaction prediction in dynamic social networksSocial Network Analysis and Mining10.1007/s13278-016-0397-y6:1Online publication date: 27-Sep-2016
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