ACTPred: activity prediction in mobile social networks

Gong, J., Tang, J. and Fong, A.C.M. (2014) ACTPred: activity prediction in mobile social networks. Tsinghua Science and Technology, 19(3), pp. 265-274. (doi: 10.1109/TST.2014.6838197)

Abstract

A current trend for online social networks is to turn mobile. Mobile social networks directly reflect our real social life, and therefore are an important source to analyze and understand the underlying dynamics of human behaviors (activities). In this paper, we study the problem of activity prediction in mobile social networks. We present a series of observations in two real mobile social networks and then propose a method, ACTPred, based on a dynamic factor-graph model for modeling and predicting users' activities. An approximate algorithm based on mean fields is presented to efficiently learn the proposed method. We deploy a real system to collect users' mobility behaviors and validate the proposed method on two collected mobile datasets. Experimental results show that the proposed ACTPred model can achieve better performance than baseline methods.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Fong, Dr Alvis Cheuk Min
• Authors: Gong, J., Tang, J., and Fong, A.C.M.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: Tsinghua Science and Technology
• ISSN: 1007-0214
• ISSN (Online): 1878-7606
• Copyright Holders: Copyright © 2014 The Authors
• First Published: First published in Tsinghua Science and Technology 19(3):264-274
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher.