We present a novel Recurrent Graph Network (RGN) approach for predicting discrete marked event sequences by learning the underlying complex stochastic process. Using the framework of Point Processes, we interpret a marked discrete event sequence as the superposition of different sequences each of a unique type. The nodes of the Graph Network use LSTM to incorporate past information whereas a Graph Attention Network (GAT Network) introduces strong inductive biases to capture the interaction between these different types of events. By changing the self-attention mechanism from attending over past events to attending over event types, we obtain a reduction in time and space complexity from $\mathcal{O}(N^2)$ (total number of events) to $\mathcal{O}(|\mathcal{Y}|^2)$ (number of event types). Experiments show that the proposed approach improves performance in log-likelihood, prediction and goodness-of-fit tasks with lower time and space complexity compared to state-of-the art Transformer based architectures.

利用点过程框架，我们提出了一种新颖的循环图网络方法来预测离散标记事件序列，其中图神经网络使用LSTM来合并过去的信息，而图注意力网络引入了强大的归纳偏置来捕捉来自不同事件类型的交互。实验结果表明，与基于Transformers的现有技术相比，所提出的方法在对数似然、预测和拟合度任务中提高了性能，并且具有更低的时间和空间复杂度。

利用循环图网络学习点过程