"ACTPred: Activity Prediction in Mobile Social Networks" by Jibing Gong, Jie Tang et al.

Article Title

ACTPred: Activity Prediction in Mobile Social Networks

Authors

Jibing Gong, School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China. The work was done when Jibing Gong was visiting Tsinghua University
Jie Tang, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China.
A. C. M. Fong, University of Glasgow in Singapore, Singapore.

Keywords

social prediction, activity prediction, user modeling, social networks

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.