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Group behavior diffusion model of social hotspots based on triadic structure and factor graphs
Author(s) -
Li Qian,
Huang Kai,
Wu Bin,
Xiao Yunpeng,
Wang Bai
Publication year - 2018
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/coin.12176
Subject(s) - computer science , diffusion , group structure , network structure , social influence , complex network , theoretical computer science , artificial intelligence , data mining , psychology , social psychology , physics , world wide web , psychotherapist , thermodynamics
In view of the complex relationships involved in network structures, as well as the Markovian nature of information diffusion and the complexity of diffusion paths in social hotspots, we introduce and optimize a basic structure in social networks—the triad. A group behavior diffusion model is proposed based on the optimal triadic structure and factor graph. In addition to predicting individual participation behavior, the model forecasts group development trends of hotspots. First, considering the dynamics of social topic information diffusion, an information triad structure is proposed based on the traditional structure for analyzing the complex influencing factors. Second, in accordance with the Markovian nature of information diffusion, a group behavior diffusion model is proposed associated with the basic concepts and methods of random field theory. This model can not only capture the strengths of different influencing factors but also mine the dynamic patterns of topics. The experimental results reveal the efficiency of our model in both predicting individual participation behavior and perceiving group evolution trends in social hotspots.