Avoiding Structural Pitfalls: Self-Supervised Low-Rank Feature Tuning for Graph Test-Time Adaptation
Authors: Haoxiang Zhang, Zhuofeng Li, Qiannan Zhang, Ziyi Kou, Juncheng Li, Shichao Pei
TMLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive experiments on six real-world datasets with diverse distribution shifts, we demonstrate that GOAT achieves consistent performance improvements across different pre-trained GNN backbones, outperforming state-of-the-art test-time adaptation methods. |
| Researcher Affiliation | Academia | Haoxiang Zhang EMAIL Halıcıoğlu Data Science Institute University of California, San Diego; Qiannan Zhang EMAIL Weill Cornell Medicine College Cornell University; Ziyi Kou EMAIL Department of Computer Science and Engineering University of Notre Dame; Juncheng Li EMAIL School of Computer Science and Technology East China Normal University; Shichao Pei EMAIL Department of Computer Science University of Massachusetts Boston |
| Pseudocode | Yes | Algorithm 1 GOAT for Full Test-Time Graph Adaptation |
| Open Source Code | No | In this section, we reveal the details of reproducing the results in the experiments. We will release the source code upon acceptance. |
| Open Datasets | Yes | We evaluate GOAT s performance on three types of distribution shifts across six benchmark datasets, following the experimental settings of EERM (Wu et al., 2023). The dataset statistics, along with a breakdown of these three distinct types of distribution shifts, are presented in Table 3: (1) Artificial Transformation for Cora (Yang et al., 2016) and Amazon-Photo (Shchur et al., 2018), where node features are replaced by synthetic features. (2) Cross-Domain transfers for Twitch-E (Rozemberczki et al., 2021) and FB-100 (Traud et al., 2012), involving graphs from different domains. (3) Temporal Evolution for Elliptic (Pareja et al., 2020) and OGB-Ar Xiv (Hu et al., 2020), utilizing dynamic datasets with natural evolving characteristics. |
| Dataset Splits | Yes | The datasets are split into training/validation/test sets with ratios of: 1/1/8 for Cora and Amazon-Photo; 1/1/5 for Twitch-E; 3/2/3 for FB-100; 5/5/33 for Elliptic; and 1/1/3 for OGBAr Xiv. |
| Hardware Specification | Yes | We perform experiments on NVIDIA Ge Force RTX 3090 GPUs. The GPU memory and running time reported in Table 3 are measured on one single RTX 3090 GPU. Additionally, we use eight CPUs, with the model name Intel(R) Xeon(R) Silver 4210R CPU @ 2.40GHz. |
| Software Dependencies | No | The operating system utilized in our experiments was Ubuntu 22.04.3 LTS (codename jammy). |
| Experiment Setup | Yes | For the setup of backbone GNNs, we majorly followed EERM (Wu et al., 2023): (a) GCN: the architecture setup is 5 layers with 32 hidden units for Elliptic and OGB-Ar Xiv, and 2 layers with 32 hidden units for other datasets, with batch normalization for all datasets. The pre-train learning rate is set to 0.001 for Cora and Amz-Photo, 0.01 for other datasets; the weight decay is set to 0 for Elliptic and OGB-Ar Xiv, and 0.001 for other datasets. ... For GOAT , we adopt Drop Edge as the augmentation function DE( ) and set the drop ratio to 0.05, K-layer aggregation in LRA set to 1 due to some GNN only has two layers in some datasets while the last GNN layer performs as a classifier head. We use Adam Optimizer for LRA module tuning. We further search the learning rate η in [1e-2, 5e-3, 1e-3, 5e-4, 1e-4, 5e-5, 1e-5, 1e-6] for different backbones, the virtual nodes number |n| in [1 , 2 , 5 , 10 , 20 ] of the class number C, the attention dim dattn in LRA in [2, 4, 8, 16, 32], total epochs T in [50, 100], and the patience in [1, 0.5, 0.1, 5e-2, 1e-2, 1e-3]. In the optimization target, we search the λ in [1, 3, 5, 10] and the α in [0.999, 0.9, 0.75, 0.5, 0.25, 0.1, 5e-2, 1e-2, 5e-3]. |