Abstract
In this paper, we proposed an agent-based model for analyzing dynamic social network associated to textual information using author-topic model, namely Textual-ABM. Author-topic model is chosen to estimate topic’s distribution transformation of agents in the agent-based model since it models the content of documents and also interests of authors. Textual-ABM can be utilized to discover dynamic of a social network which includes not only network structure but also node’s properties over time. Furthermore, we introduced independent cascade model based on homophily, namely H-IC. The infected probability associated with each edge is homophily or similarity which measured based on topic’s distribution. We have applied our methodology to a collected data set from NIPS and have obtained satisfactory results.
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Ho, K.T., Bui, Q.V., Bui, M. (2018). Dynamic Social Network Analysis Using Author-Topic Model. In: Hodoň, M., Eichler, G., Erfurth, C., Fahrnberger, G. (eds) Innovations for Community Services. I4CS 2018. Communications in Computer and Information Science, vol 863. Springer, Cham. https://doi.org/10.1007/978-3-319-93408-2_4
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