Matcha: Mitigating Graph Structure Shifts with Test-Time Adaptation
Authors: Wenxuan Bao, Zhichen Zeng, Zhining Liu, Hanghang Tong, Jingrui He
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on synthetic and real-world datasets to evaluate our proposed Matcha from the following aspects: RQ1: How can Matcha empower TTA algorithms and handle various structure shifts on graphs? RQ2: To what extent can Matcha restore the representation quality better than other methods? |
| Researcher Affiliation | Academia | 1University of Illinois Urbana-Champaign EMAIL |
| Pseudocode | Yes | Algorithm 1 Matcha |
| Open Source Code | Yes | Our code is available at https://github.com/baowenxuan/Matcha. |
| Open Datasets | Yes | We first adopt CSBM (Deshpande et al., 2018) to generate synthetic graphs with controlled structure and attribute shifts. ... For real-world datasets, we adopt Syn-Cora (Zhu et al., 2020), Syn-Products (Zhu et al., 2020), Twitch-E (Rozemberczki et al., 2021), and OGB-Arxiv (Hu et al., 2020). |
| Dataset Splits | Yes | We use non-overlapping train-test split over nodes on Syn-Cora to avoid label leakage. ... For OGB-Arxiv, we use a subgraph consisting of papers from 1950 to 2011 as the source graph, 2011 to 2014 as the validation graph, and 2014 to 2020 as the target graph. |
| Hardware Specification | Yes | We use single Nvidia Tesla V100 with 32GB memory. However, for the majority of our experiments, the memory usage should not exceed 8GB. We switch to Intel(R) Xeon(R) Gold 6240R CPU @ 2.40GHz when recording the computation time. |
| Software Dependencies | No | The paper does not explicitly mention specific software dependencies with version numbers. |
| Experiment Setup | Yes | For CSBM, Syn-Cora, Syn-Products, we use GPRGNN with K = 9. The featurizer is a linear layer, followed by a batchnorm layer, and then the GPR module. The classifier is a linear layer. The dimension for representation is 32. For Twitch-E and OGB-Arxiv, we use GPRGNN with K = 5. The dimension for representation is 8 and 128, respectively. |