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

by   Aravind Sankar, et al.

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 diffusion prediction models; we achieve massive gains for users with sparse activities, and users who lack direct social neighbors in seed sets.



There are no comments yet.


page 8


Independent Asymmetric Embedding Model for Cascade Prediction on Social Network

The prediction for information diffusion on social networks has great pr...

DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation

Social recommendation has emerged to leverage social connections among u...

Adversarial and Contrastive Variational Autoencoder for Sequential Recommendation

Sequential recommendation as an emerging topic has attracted increasing ...

Global-Local Item Embedding for Temporal Set Prediction

Temporal set prediction is becoming increasingly important as many compa...

DyHGCN: A Dynamic Heterogeneous Graph Convolutional Network to Learn Users' Dynamic Preferences for Information Diffusion Prediction

Information diffusion prediction is a fundamental task for understanding...

Exploring Social Posterior Collapse in Variational Autoencoder for Interaction Modeling

Multi-agent behavior modeling and trajectory forecasting are crucial for...

Reconnecting the Estranged Relationships: Optimizing the Influence Propagation in Evolving Networks

Influence Maximization (IM), which aims to select a set of users from a ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.