Abstract
In this research, we proposed homophily independent cascade model based on textual information, namely Textual-Homo-IC. This model based on standard independent cascade model; however, we exploited the aspect of infected probability estimation relied on homophily. Particularly, homophily is measured based on textual content by utilizing topic modeling. The process of propagation takes place on agent’s network where each agent represents a node. In addition to expressing the Textual-Homo-IC model on the static network, we also revealed it on dynamic agent’s network where there is not only transformation of the structure but also the node’s properties during the spreading process. We conducted experiments on two collected data sets from NIPS and a social network platform-Twitter and have attained satisfactory results.
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Ho, T.K.T., Bui, Q.V., Bui, M. (2018). Homophily Independent Cascade Diffusion Model Based on Textual Information. In: Nguyen, N., Pimenidis, E., Khan, Z., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2018. Lecture Notes in Computer Science(), vol 11055. Springer, Cham. https://doi.org/10.1007/978-3-319-98443-8_13
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