Inferring latent co-activation patterns for information diffusion

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

1 Citation (Scopus)

Abstract

Different diffusion models have been proposed in previous literature to model information diffusion, in which each node is often assumed to be independently influenced by its parents. More recently, some have begun to challenge this assumption based on the observation that structural and behavioral dependency among the parent nodes exerts a notable role in diffusion within networks. In this paper, we postulate that a node is independently influenced by a set of latent co-activation patterns of its parents, instead of the parents directly. We integrate the latent class model with the conventional independent cascade model where each latent class corresponds to a particular co-activation pattern of the parent nodes. Each parent activation is essentially first "projected" onto the latent space and then "reconstructed" before exerting its influence onto the child nodes. The coactivation patterns are to be inferred based on the information cascades observed without using the connectivity related cues except the information of direct parents. We formulate the co-activation pattern identification problem and the diffusion network inference problem under a unified probabilistic framework. A two-level EM algorithm is derived for inferring the model parameters. We applied the proposed model to a meme dataset and two social network datasets with promising results obtained. Using the results obtained based on the meme dataset, we also illustrate how the identified co-activation patterns can support the analysis of dependency among online news media.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages485-492
Number of pages8
ISBN (Electronic)9781467396172
DOIs
Publication statusPublished - 2 Feb 2016
Event2015 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology Workshops, WI-IAT Workshops 2015 - Singapore, Singapore
Duration: 6 Dec 20159 Dec 2015

Publication series

NameProceedings - 2015 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2015
Volume1

Conference

Conference2015 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology Workshops, WI-IAT Workshops 2015
Country/TerritorySingapore
CitySingapore
Period6/12/159/12/15

Scopus Subject Areas

  • Computer Networks and Communications
  • Software

Fingerprint

Dive into the research topics of 'Inferring latent co-activation patterns for information diffusion'. Together they form a unique fingerprint.

Cite this