@inproceedings{8a369eab3f6d4aff89dd36cfce7baecf,
title = "Inferring motif-based diffusion models for social networks",
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.",
author = "Qing Bao and CHEUNG, {Kwok Wai} and Jiming LIU",
note = "Copyright: Copyright 2017 Elsevier B.V., All rights reserved.; 25th International Joint Conference on Artificial Intelligence, IJCAI 2016 ; Conference date: 09-07-2016 Through 15-07-2016",
year = "2016",
month = jul,
language = "English",
isbn = "9781577357711",
volume = "2016-January",
series = "IJCAI International Joint Conference on Artificial Intelligence",
publisher = "AAAI press",
pages = "3677--3683",
booktitle = "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16)",
}