Department of Computer Science
Inferring motif-based diffusion models for social networks
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.
Source Publication Title
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16)
Kambhampati, Subbarao ; Arizona State University
New York, United States
AAAI Press / International Joint Conferences on Artificial Intelligence
Copyright © 2016 International Joint Conferences on Artificial Intelligence All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher.
This work was partially supported by Hong Kong Baptist University Strategic Development Fund.
Link to Publisher's Edition
Bao, Qing, William K. Cheung, and Jiming Liu. "Inferring motif-based diffusion models for social networks." Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16) (2006): 3677-3683.