Invariant Graph Representations for Continuous-Time Dynamic Graphs Under Distribution Shifts

The article presents CIR, a novel framework for learning invariant representations in Continuous-Time Dynamic Graphs (CTDGs) under out-of-distribution (OOD) shifts, utilizing a structural causal model called ICCM. It incorporates the Normalized Weighted Geometric Mean (NWGM) for efficient interventional predictions and employs a deep learning architecture with subgraph extractors and an environment memory bank to handle distributional shifts. This advancement is significant for practitioners as it enhances the robustness and applicability of CTDG models in dynamic environments, addressing limitations of existing methods in OOD scenarios.