Predicting what becomes popular on social media is crucial because it helps us understand future topics and public interests based on massive social data. Previous studies mainly focused on picking specific features and checking past statistic numbers, ignoring the hidden impact of messages passing along the complex relationships among different entities. People talk and connect with others on social media; thus, it is essential to consider how information spreads when studying social media networks. This work proposes a multi-layer temporal graph neural network (GNN) framework for predicting what will be popular on social media networks. This framework takes into account the way information spreads among different entities. The proposed method involves multi-layer relations and temporal information within a sequence of social media network snapshots. It learns the temporal representations of target entities in each snapshot and predicts how the popularity of a particular entity will change in future snapshots. The proposed method is evaluated with real-world data across four popularity trend prediction tasks. The experimental results prove that the proposed method performs better than various baselines, including traditional machine learning regression approaches, prior methods for popularity trend prediction, and other GNN models.

预测社交媒体上流行的内容至关重要，因为它有助于我们根据海量社交数据了解未来的话题和公共利益。以前的研究主要集中在挑选特定特征并检查过去的统计数字，忽略了消息在不同实体之间传递复杂关系的隐藏影响。人们在社交媒体上与他人交谈和联系；因此，在研究社交媒体网络时，必须考虑信息如何传播。这项工作提出了一种多层时间图神经网络（GNN）框架，用于预测社交媒体网络上的流行内容。该框架考虑了信息在不同实体之间传播的方式。所提出的方法涉及社交媒体网络快照序列中的多层关系和时间信息。它学习每个快照中目标实体的时间表示，并预测特定实体的流行度在未来快照中将如何变化。所提出的方法使用四个流行趋势预测任务的真实数据进行评估。实验结果证明，该方法的性能优于各种基线，包括传统的机器学习回归方法、流行趋势预测的现有方法以及其他 GNN 模型。