Inf-VAE: A Variational Autoencoder Framework to Integrate Homophily and Influence in Diffusion Prediction

TL;DR

Inf-VAE is a novel variational autoencoder framework that effectively integrates social homophily and influence to improve diffusion prediction accuracy, especially for inactive users with limited activity history.

Contribution

This paper introduces Inf-VAE, a new framework combining social and temporal embeddings via graph neural networks and co-attentive fusion for enhanced diffusion prediction.

Findings

Achieves 22% improvement in MAP@10 over state-of-the-art models.

Significantly better performance for inactive users with sparse activities.

Effective in datasets like Digg, Weibo, and Stack-Exchanges.

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.