A framework for information dissemination in social networks using Hawkes processes

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• 作者：J.C. Louzada Pinto^a ; ^{julio.louzada_pinto@telecom-sudparis.eu" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; T. Chahed^a ; ^{tijani.chahed@telecom-sudparis.eu" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; E. Altman^b ; ^{eitan.altman@inria.fr" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：Information diffusion ; Social networks ; Hawkes processes ; Nonnegative tensor factorization ; Topic models
• 刊名：Performance Evaluation
• 出版年：2016
• 出版时间：September 2016
• 年：2016
• 卷：103
• 期：Complete
• 页码：86-107
• 全文大小：2643 K

文摘

We define in this paper a general Hawkes-based framework to model information diffusion in social networks. The proposed framework takes into consideration the hidden interactions between users as well as the interactions between contents and social networks, and can also accommodate dynamic social networks and various temporal effects of the diffusion, which provides a complete analysis of the hidden influences in social networks. This framework can be combined with topic modeling, for which modified collapsed Gibbs sampling and variational Bayes techniques are derived. We provide an estimation algorithm based on nonnegative tensor factorization techniques, which together with a dimensionality reduction argument are able to discover, in addition, the latent community structure of the social network. At last, we provide numerical examples from real-life networks: a Game of Thrones and a MemeTracker datasets.