InF-VAE: A variational autoencoder framework to integrate homophily and influence in diffusion prediction

Aravind Sankar, Xinyang Zhang, Adit Krishnan, Jiawei Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recent years have witnessed tremendous interest in understanding and predicting information spread on social media platforms such as Twitter, Facebook, etc. Existing diffusion prediction methods primarily exploit the sequential order of influenced users by projecting diffusion cascades onto their local social neighborhoods. However, this fails to capture global social structures that do not explicitly manifest in any of the cascades, resulting in poor performance for inactive users with limited historical activities. In this paper, we present a novel variational autoencoder framework (Inf-VAE) to jointly embed homophily and influence through proximity-preserving social and position-encoded temporal latent variables. To model social homophily, Inf-VAE utilizes powerful graph neural network architectures to learn social variables that selectively exploit the social connections of users. Given a sequence of seed user activations, Inf-VAE uses a novel expressive co-attentive fusion network that jointly attends over their social and temporal variables to predict the set of all influenced users. Our experimental results on multiple real-world social network datasets, including Digg, Weibo, and Stack-Exchanges demonstrate significant gains (22% MAP@10) for Inf-VAE over state-of-the-art diffusion prediction models; we achieve massive gains for users with sparse activities, and users who lack direct social neighbors in seed sets.

Original languageEnglish (US)
Title of host publicationWSDM 2020 - Proceedings of the 13th International Conference on Web Search and Data Mining
PublisherAssociation for Computing Machinery, Inc
Pages510-518
Number of pages9
ISBN (Electronic)9781450368223
DOIs
StatePublished - Jan 20 2020
Event13th ACM International Conference on Web Search and Data Mining, WSDM 2020 - Houston, United States
Duration: Feb 3 2020Feb 7 2020

Publication series

NameWSDM 2020 - Proceedings of the 13th International Conference on Web Search and Data Mining

Conference

Conference13th ACM International Conference on Web Search and Data Mining, WSDM 2020
CountryUnited States
CityHouston
Period2/3/202/7/20

Keywords

  • Attention
  • Autoencoder
  • Deep learning
  • Diffusion
  • Social network

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software
  • Computer Science Applications

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  • Cite this

    Sankar, A., Zhang, X., Krishnan, A., & Han, J. (2020). InF-VAE: A variational autoencoder framework to integrate homophily and influence in diffusion prediction. In WSDM 2020 - Proceedings of the 13th International Conference on Web Search and Data Mining (pp. 510-518). (WSDM 2020 - Proceedings of the 13th International Conference on Web Search and Data Mining). Association for Computing Machinery, Inc. https://doi.org/10.1145/3336191.3371811