Inferring Motif-Based Diffusion Models for Social Networks 机翻标题: 暂无翻译,请尝试点击翻译按钮。

会议集名/来源
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence: IJCAI-16, New York City, New York, USA, 9-15 July 2016, Volume Five
出版年
2016
页码
3677-3683
会议地点
New York
语种
eng
作者单位
Dept. of Computer Science, Hong Kong Baptist University;Dept. of Computer Science, Hong Kong Baptist University;Dept. of Computer Science, Hong Kong Baptist University
作者
Qing Bao;William K. Cheung;Jiming Liu
摘要
Existing diffusion models for social networks often assume that the activation of a node depends independently on their parents' activations. Some recent work showed that incorporating the structural and behavioral dependency among the parent nodes allows more accurate diffusion models to be inferred. In this paper, we postulate that the latent temporal activation patterns (or motifs) of nodes of different social roles form the underlying information diffusion mechanisms generating the information cascades observed over a social network. We formulate the inference of the temporal activation motifs and a corresponding motif-based diffusion model under a unified probabilistic framework. A two-level EM algorithm is derived so as to infer the diffusion-specific motifs and the diffusion probabilities simultaneously. We applied the proposed model to several real-world datasets with significant improvement on modelling accuracy. We also illustrate how the inferred motifs can be interpreted as the underlying mechanisms causing the diffusion process to happen in different social networks.
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