用图神经网络捕捉用户与物品的动态交互关系，提升时间事件预测精度。
Deep Graph Neural Point Process For Learning Temporal Interactive Networks
- 结合图结构与注意力机制，同时建模静态拓扑与动态演化
- 在三个数据集上显著优于基线模型，事件与时间预测更准确
- 适合做推荐系统、社交网络等时序交互建模的研究者参考
以往学习时序交互网络（TIN）被视为粗粒度的多序列预测问题，忽略了网络拓扑结构的影响。本文提出深度图神经点过程（DGNPP）模型以解决这一局限。DGNPP包含两个核心模块：节点聚合层用于捕获拓扑结构，生成用户与物品的静态表示；自注意层则动态更新嵌入表示。通过将动态与静态嵌入融合进事件强度函数，并采用最大似然估计优化模型，DGNPP能有效预测事件及其发生时间。在三个公开数据集上的实验表明，DGNPP在事件预测和时间预测任务中均表现优异，效率高，显著超越基线模型，有效缓解了先前方法的不足。
原文摘要 · Abstract (English)
Learning temporal interaction networks(TIN) is previously regarded as a coarse-grained multi-sequence prediction problem, ignoring the network topology structure influence. This paper addresses this limitation and a Deep Graph Neural Point Process(DGNPP) model for TIN is proposed. DGNPP consists of two key modules: the Node Aggregation Layer and the Self Attentive Layer. The Node Aggregation Layer captures topological structures to generate static representation for users and items, while the Self Attentive Layer dynamically updates embeddings over time. By incorporating both dynamic and static embeddings into the event intensity function and optimizing the model via maximum likelihood estimation, DGNPP predicts events and occurrence time effectively. Experimental evaluations on three public datasets demonstrate that DGNPP achieves superior performance in event prediction and time prediction tasks with high efficiency, significantly outperforming baseline models and effectively mitigating the limitations of prior approaches.
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