Popularity Prediction on Social Platforms with Coupled Graph Neural Networks
This work addresses the problem of more accurate popularity prediction for researchers and practitioners on social platforms, representing an incremental improvement by focusing on network-aware cascading effects.
The paper tackles the problem of predicting online content popularity on social platforms by explicitly modeling the cascading effect in information diffusion, using a novel method called CoupledGNN that leverages coupled graph neural networks. The result shows that this method significantly outperforms state-of-the-art methods on synthetic and real-world Sina Weibo datasets.
Predicting the popularity of online content on social platforms is an important task for both researchers and practitioners. Previous methods mainly leverage demographics, temporal and structural patterns of early adopters for popularity prediction. However, most existing methods are less effective to precisely capture the cascading effect in information diffusion, in which early adopters try to activate potential users along the underlying network. In this paper, we consider the problem of network-aware popularity prediction, leveraging both early adopters and social networks for popularity prediction. We propose to capture the cascading effect explicitly, modeling the activation state of a target user given the activation state and influence of his/her neighbors. To achieve this goal, we propose a novel method, namely CoupledGNN, which uses two coupled graph neural networks to capture the interplay between node activation states and the spread of influence. By stacking graph neural network layers, our proposed method naturally captures the cascading effect along the network in a successive manner. Experiments conducted on both synthetic and real-world Sina Weibo datasets demonstrate that our method significantly outperforms the state-of-the-art methods for popularity prediction.