  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.

Machine Learning