We design a contrastive modularity-based graph clustering method to infer the environment labels of nodes for the graph with complex multiple latent ...
In this paper, we propose a novel I nvariant N ode representation L earning ( INL ) approach capable of generating invariant node representations based on the ...
Invariant Node Representation Learning under Distribution Shifts with Multiple Latent Environments. H Li, Z Zhang, X Wang, W Zhu. ACM Transactions on ...
Invariant Node Representation Learning under Distribution Shifts with Multiple Latent Environments. TOIS, 2023. (Full Paper, CCF-A) (Paper). 2022. [C17] ...
Oct 31, 2022 · We propose Graph Invariant Learning (GIL) model capable of learning generalized graph representations under distribution shifts.
We propose a novel Graph Invariant Learning method (GIL) to learn invariant and OOD generalized graph representations under distribution shifts. To the best of ...
Oct 25, 2024 · In our survey, we provide an up-to-date and forward-looking review of deep graph learning under distribution shifts.
Our proposed method jointly optimizes three modules: (1). In the invariant subgraph identification module, a GNN-based subgraph generator Φ(·) identifies the.
(TOIS'24) Invariant node representation learning under distribution shifts with multiple latent environments. (KBS'24) Fortune favors the invariant: Enhancing ...
In this paper, we formulate the OOD problem on graphs and develop a new invariant learning approach, Explore-to-Extrapolate Risk Minimization (EERM), that ...