Document Type

Conference Paper

Department/Unit

Department of Computer Science; Department of Journalism

Title

Inferring latent co-activation patterns for information diffusion

Language

English

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.

Keywords

Integrated circuit modeling, Biological system modeling, Media, Social network services, Mathematical model, Analytical models

Publication Date

12-2015

Source Publication Title

2015 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology

Start Page

485

End Page

492

Conference Location

Singapore

Publisher

IEEE Computer Society

Copyright

Copyright © 2015 by The Institute of Electrical and Electronics Engineers, Inc.

DOI

10.1109/WI-IAT.2015.115

Link to Publisher's Edition

http://dx.doi.org/10.1109/WI-IAT.2015.115

ISBN (print)

9781467396172

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