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.

在这篇论文中，我们提出了一种名为 CoupledGNN 的新方法，该方法使用两个耦合的图神经网络来捕捉节点激活状态和影响传播之间的相互作用，从而精确地预测社交平台上的信息传播。我们在 Sina Weibo 数据集上进行的实验证明，与各种现有方法相比，我们的方法显著提高了流行度预测的准确性。