Detecting Community and Topic Co-Evolution in Social Networks

Juan Bi, Zhiguang Qin, Jia Huang

Abstract


In this paper we study how to discover the co-evolution of topics and communities over time in dynamic social networks. We present a topic model-based approach that automatically captures the dynamic features of communities and topics evolution. Our model can be viewed as an extension of the LDA model with the key addition that it can not only detect communities and topics simultaneously but also work in an online fashion. Instead of modeling communities and topics in statistical manner, the proposed model can simulate the user’s interests drifting at different time epochs by taking into consideration the temporal information implied in the data, and observe how the community structure changes over time with the evolution of topics. Experiments on real-world data set have proved the ability of this model in discovering well-connected and topically meaningful communities and the co-evolution pattern of topics and communities.

 

DOI : http://dx.doi.org/10.11591/telkomnika.v12i5.4389

 


Keywords


Community Discovery, LDA, Probabilistic Generative Model, Social Networks

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Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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