Handling Distribution Shifts on Graphs: An Invariance Perspective
Authors: Qitian Wu, Hengrui Zhang, Junchi Yan, David Wipf
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | 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 echo740@sjtu.edu.cn Hengrui Zhang University of Illinois at Chicago hzhan55@uic.edu Junchi Yan Shanghai Jiao Tong University yanjunchi@sjtu.edu.cn David Wipf Amazon daviwipf@amazon.com |
| 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}. |