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..
Fast Graph Sharpness-Aware Minimization for Enhancing and Accelerating Few-Shot Node Classification
Authors: Yihong Luo, Yuhan Chen, Siya Qiu, Yiwei Wang, Chen Zhang, Yan Zhou, Xiaochun Cao, Jing Tang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that our proposed algorithm outperforms the standard SAM with lower computational costs in FSNC tasks. |
| Researcher Affiliation | Collaboration | 1 The Hong Kong University of Science and Technology 2 The Hong Kong University of Science and Technology (Guangzhou) 3 School of Computer Science and Engineering, Sun Yat-sen University 4 University of California, Merced 5 University of California, Los Angeles 6 Createlink Technology 7 School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University |
| Pseudocode | Yes | Algorithm 1 Training with FGSAM and FGSAM+. |
| Open Source Code | Yes | The code is available at https://github.com/draym28/FGSAM_NeurIPS24 |
| Open Datasets | Yes | We conduct evaluations on three widely used real-world benchmark node classification datasets: Cora Full [5], DBLP and ogbn-ar Xiv [18] |
| Dataset Splits | Yes | we use the train/val/test split as in [34] and [24]. We further split Cbase into two disjoint class set: training class set Ctr and validation class set Cval, such that Cbase = Ctr Cval and Ctr Cval = Ø. Overall, we use Ctr and Cval for train and validation in the meta-training stage, respectively, and use Cnovel for meta-test. We split C into Ctr, Cval and Cnovel according to the class split ratio in Tab. 5. |
| Hardware Specification | Yes | We implement our model by Py Torch [29] and conduct experiments on an RTX-3090Ti. |
| Software Dependencies | No | We implement our model by Py Torch [29] and conduct experiments on an RTX-3090Ti. We use Optuna [2] to search the hyper-parameters for each setting. |
| Experiment Setup | Yes | We use Optuna [2] to search the hyper-parameters for each setting. See Appendix D.2 for detailed FSNC learning protocol. Table 6: Hyper-parameters Search Space. |