Dynamic graphs arise in various real-world applications, and it is often welcomed to model the dynamics directly in continuous time domain for its flexibility. This paper aims to design an easy-to-use pipeline (termed as EasyDGL which is also due to its implementation by DGL toolkit) composed of three key modules with both strong fitting ability and interpretability. Specifically the proposed pipeline which involves encoding, training and interpreting: i) a temporal point process (TPP) modulated attention architecture to endow the continuous-time resolution with the coupled spatiotemporal dynamics of the observed graph with edge-addition events; ii) a principled loss composed of task-agnostic TPP posterior maximization based on observed events on the graph, and a task-aware loss with a masking strategy over dynamic graph, where the covered tasks include dynamic link prediction, dynamic node classification and node traffic forecasting; iii) interpretation of the model outputs (e.g., representations and predictions) with scalable perturbation-based quantitative analysis in the graph Fourier domain, which could more comprehensively reflect the behavior of the learned model. Extensive experimental results on public benchmarks show the superior performance of our EasyDGL for time-conditioned predictive tasks, and in particular demonstrate that EasyDGL can effectively quantify the predictive power of frequency content that a model learn from the evolving graph data.

翻译：动态图在各类实际应用中广泛存在，由于连续时间域建模具有灵活性，直接在该域中对动态性进行建模备受青睐。本文旨在设计一个易用型流水线（命名为EasyDGL，亦因其基于DGL工具包实现），它包含三个兼具强拟合能力与可解释性的核心模块。具体而言，该流水线涉及编码、训练与解释：i) 采用时序点过程调制的注意力架构，为边缘添加事件观测图中的耦合时空动态赋予连续时间分辨率；ii) 构建由基于图观测事件的任务无关型TPP后验最大化损失与动态图掩码策略下的任务感知损失组成的原理性损失函数，涵盖动态链接预测、动态节点分类及节点流量预测等任务；iii) 通过在图上弗域中进行可扩展的扰动定量分析，实现模型输出（如表示与预测）的解释，该分析能更全面反映所学模型的行为。在公开基准上的大量实验结果表明，EasyDGL在时间条件预测任务中表现优异，尤其证明了其能有效量化模型从演化图数据中学习的频率内容的预测能力。