Environment-Aware Dynamic Graph Learning for Out-of-Distribution
  Generalization
Haonan Yuan Qingyun Sun Xingcheng Fu Ziwei Zhang Cheng Ji Hao Peng Jianxin Li
Dynamic graph neural networks (DGNNs) are increasingly pervasive in
exploiting spatio-temporal patterns on dynamic graphs. However, existing works
fail to generalize under distribution shifts, which are common in real-world
scenarios. As the generation of dynamic graphs is heavily influenced by latent
environments, investigating their impacts on the out-of-distribution (OOD)
generalization is critical. However, it remains unexplored with the following
two major challenges: (1) How to properly model and infer the complex
environments on dynamic graphs with distribution shifts? (2) How to discover
invariant patterns given inferred spatio-temporal environments? To solve these
challenges, we propose a novel Environment-Aware dynamic Graph LEarning (EAGLE)
framework for OOD generalization by modeling complex coupled environments and
exploiting spatio-temporal invariant patterns. Specifically, we first design
the environment-aware EA-DGNN to model environments by multi-channel
environments disentangling. Then, we propose an environment instantiation
mechanism for environment diversification with inferred distributions. Finally,
we discriminate spatio-temporal invariant patterns for out-of-distribution
prediction by the invariant pattern recognition mechanism and perform
fine-grained causal interventions node-wisely with a mixture of instantiated
environment samples. Experiments on real-world and synthetic dynamic graph
datasets demonstrate the superiority of our method against state-of-the-art
baselines under distribution shifts. To the best of our knowledge, we are the
first to study OOD generalization on dynamic graphs from the environment
learning perspective.