Towards Robust Graph Incremental Learning on Evolving Graphs
Authors: Junwei Su, Difan Zou, Zijun Zhang, Chuan Wu
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through comprehensive empirical studies with several benchmark datasets, we demonstrate that our proposed method, Structural-Shift-Risk Mitigation (SSRM), is flexible and easy to adapt to improve the performance of state-of-the-art GNN incremental learning frameworks in the inductive setting. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Hong Kong 2Department of Computer Science, University of Wu Han. |
| Pseudocode | Yes | The overall learning procedure in each stage is summarized in Algorithm 1 and Fig. 6 provide a graphical illustration of the procedure. |
| Open Source Code | Yes | Implementation available at: https://github.com/littleTown93/NGIL_Evolve |
| Open Datasets | Yes | We evaluate our proposed method, SSRM, on OGB-Arxiv (Hu et al., 2020), Reddit (Hamilton et al., 2017), and Cora Full (Bojchevski & G unnemann, 2017). |
| Dataset Splits | Yes | For all the datasets, the train-validation-test splitting ratios are 60%, 20%, and 20%. |
| Hardware Specification | Yes | All the experiments of this paper are conducted on the following machine CPU: two Intel Xeon Gold 6230 2.1G, 20C/40T, 10.4GT/s, 27.5M Cache, Turbo, HT (125W) DDR4-2933 GPU: four NVIDIA Tesla V100 SXM2 32G GPU Accelerator for NV Link Memory: 256GB (8 x 32GB) RDIMM, 3200MT/s, Dual Rank OS: Ubuntu 18.04LTS |
| Software Dependencies | No | The paper mentions 'OS: Ubuntu 18.04LTS' in the hardware specifications, but does not list specific version numbers for software libraries or dependencies like Python, PyTorch, or other relevant packages used for the experiments. |
| Experiment Setup | Yes | We use α = 0.1, β = 0.5 for SSRM. Table 4 is the hyperparameter research space we adopt from (Zhang et al., 2022). Table 4. Incremental learning settings for each dataset. GEM memory strength:[0.05,0.5,5]; n memories:[10,100,1000] TWP lambda 1:[100,10000]; lambda t:[100,10000]; beta:[0.01,0.1] ER-GNN budget:[10,100]; d:[0.05,0.5,5.0]; sampler:[CM] |