Detecting Community and Topic Co-Evolution in Social Networks - Juan Bi; Zhiguang Qin; Jia Huang (Indonesian Journal of Electrical Engineering and Computer Science, 2014)
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