A User Participation Behavior Prediction Model of Social Hotspots Based on Influence and Markov Random Field  

作　　者：Yunpeng Xiao Jiawei Lai Yanbing Liu 

机构地区：[1]Chongqing Engineering Laboratory of Network and Information Security,Chongqing University of Posts and Telecommunications,Chongqing 400065,China

出　　处：《China Communications》2017年第5期145-159,共15页中国通信（英文版）

基　　金：supported by the National Key Basic Research Program(973 program)of China(No.2013CB329606);National Science Foundation of China(Grant No.61272400);Science and Technology Research Program of the Chongqing Municipal Education Committee(No.KJ1500425);Wen Feng Foundation of CQUPT(No.WF201403);Chongqing Graduate Research And Innovation Project(No.CYS14146)

摘　　要：Hotspot topic trends can be captured by analyzing user attributes and historical behavior in social network. In this paper, we propose a user participation behavior prediction model for social hotspots, based on user behavior and relationship data, to predict user participation behavior and topic development trends. Firstly, for the complex factors of user behavior, three dynamic influence factor functions are defined, including individual, peer and community influence. These functions take timeliness into account using a time discretization method. Secondly, to determine laws of individual behavior and group behavior within a social topic, a hotspot user participation behavior prediction model is proposed and associated with the basic concepts of randora field and Markov property in information diffusion. The experimental results show that the model can not only dynamically predict the individual behavior, but also grasp the development trends of topics.Hotspot topic trends can be captured by analyzing user attributes and historical behavior in social network. In this paper,we propose a user participation behavior prediction model for social hotspots,based on user behavior and relationship data,to predict user participation behavior and topic development trends. Firstly,for the complex factors of user behavior,three dynamic influence factor functions are defined,including individual,peer and community influence. These functions take timeliness into account using a time discretization method. Secondly,to determine laws of individual behavior and group behavior within a social topic,a hotspot user participation behavior prediction model is proposed and associated with the basic concepts of random field and Markov property in information diffusion. The experimental results show that the model can not only dynamically predict the individual behavior,but also grasp the development trends of topics.

关 键 词：social network hotspot topic behavior prediction Markov random field influence factor 

分 类 号：TP393.09[自动化与计算机技术—计算机应用技术]