Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Handling Distribution Shifts on Graphs: An Invariance Perspective
Authors: Qitian Wu, Hengrui Zhang, Junchi Yan, David Wipf
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We prove the validity of our method by theoretically showing its guarantee of a valid OOD solution and further demonstrate its power on various real-world datasets for handling distribution shifts from artificial spurious features, cross-domain transfers and dynamic graph evolution. |
| Researcher Affiliation | Collaboration | Qitian Wu Shanghai Jiao Tong University EMAIL Hengrui Zhang University of Illinois at Chicago EMAIL Junchi Yan Shanghai Jiao Tong University EMAIL David Wipf Amazon EMAIL |
| Pseudocode | Yes | Algorithm 1: Stable Learning for OOD Generalization in Node-Level Prediction on Graphs. |
| Open Source Code | Yes | 1The implementation is public available at https://github.com/qitianwu/Graph OOD-EERM. |
| Open Datasets | Yes | We adopt two public social network datasets Twitch-Explicit and Facebook-100 collected by Lim et al. (2021). |
| Dataset Splits | Yes | We generate 10-fold graph data with distinct environment id s and use 1/1/8 of them for training/validation/testing. |
| Hardware Specification | Yes | Most of our experiments are run on Ge Force RTX 2080Ti with 11GB except some experiments requiring large GPU memory for which we adopt RTX 8000 with 48GB. |
| Software Dependencies | Yes | The configurations of our environments and packages are listed below: Ubuntu 16.04, Numpy 1.20.3, Py Torch 1.9.0, Py Torch Geometric 1.7.2 |
| Experiment Setup | Yes | Other hyper-parameters are searched with grid search on validation dataset. The searching space are as follows: learning rate for GNN backbone αf {0.0001, 0.0002, 0.001, 0.005, 0.01}, learning rate for graph editers αg {0.0001, 0.001, 0.005, 0.01}, weight for combination β {0.2, 0.5, 1.0, 2.0, 3.0}, number of edge editing for each node s {1, 5, 10}, number of iterations for inner update before one-step outer update T {1, 5}. |