MCDAN: a Multi-scale Context-enhanced Dynamic Attention Network for Diffusion Prediction

TL;DR

This paper introduces MCDAN, a novel neural network that enhances diffusion prediction by integrating multi-scale user preferences, social relationships, and user susceptibility, significantly outperforming existing models.

Contribution

The paper proposes MCDAN, a multi-scale context-enhanced dynamic attention network that incorporates social and cascade relationships, user preferences, and susceptibility for improved diffusion prediction.

Findings

MCDAN outperforms state-of-the-art models by up to 10.61% in Hits@100.

The model achieves a 9.71% improvement in MAP@100.

Experiments on four datasets validate the effectiveness of the proposed approach.

Abstract

Information diffusion prediction aims at predicting the target users in the information diffusion path on social networks. Prior works mainly focus on the observed structure or sequence of cascades, trying to predict to whom this cascade will be infected passively. In this study, we argue that user intent understanding is also a key part of information diffusion prediction. We thereby propose a novel Multi-scale Context-enhanced Dynamic Attention Network (MCDAN) to predict which user will most likely join the observed current cascades. Specifically, to consider the global interactive relationship among users, we take full advantage of user friendships and global cascading relationships, which are extracted from the social network and historical cascades, respectively. To refine the model's ability to understand the user's preference for the current cascade, we propose a multi-scale sequential hypergraph attention module to capture the dynamic preference of users at different time scales. Moreover, we design a contextual attention enhancement module to strengthen the interaction of user representations within the current cascade. Finally, to engage the user's own susceptibility, we construct a susceptibility label for each user based on user susceptibility analysis and use the rank of this label for auxiliary prediction. We conduct experiments over four widely used datasets and show that MCDAN significantly overperforms the state-of-the-art models. The average improvements are up to 10.61% in terms of Hits@100 and 9.71% in terms of MAP@100, respectively.