Inferring motif-based diffusion models for social networks

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

5 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16)
PublisherAAAI press
Pages3677-3683
Number of pages7
Volume2016-January
ISBN (Print)9781577357711
Publication statusPublished - Jul 2016
Event25th International Joint Conference on Artificial Intelligence, IJCAI 2016 - New York, United States, New York, United States
Duration: 9 Jul 201615 Jul 2016

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
ISSN (Print)1045-0823

Conference

Conference25th International Joint Conference on Artificial Intelligence, IJCAI 2016
Country/TerritoryUnited States
CityNew York
Period9/07/1615/07/16

Scopus Subject Areas

  • Artificial Intelligence

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