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..
Towards Robust Graph Incremental Learning on Evolving Graphs
Authors: Junwei Su, Difan Zou, Zijun Zhang, Chuan Wu
ICML 2023 | Venue PDF | 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] |