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..
ImGCL: Revisiting Graph Contrastive Learning on Imbalanced Node Classification
Authors: Liang Zeng, Lanqing Li, Ziqi Gao, Peilin Zhao, Jian Li
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on multiple imbalanced graph datasets and imbalanced settings demonstrate the effectiveness of our proposed framework, which significantly improves the performance of the recent state-of-the-art GCL methods. |
| Researcher Affiliation | Collaboration | 1Institute for Interdisciplinary Information Sciences (IIIS), Tsinghua University 2Tencent AI Lab 3Hong Kong University of Science and Technology |
| Pseudocode | Yes | Algorithm 1: The Im GCL pre-training algorithm |
| Open Source Code | No | The paper mentions using 'Py GCL (Zhu et al. 2021a) open-source library' for baselines but does not provide a link or explicit statement about the availability of the source code for their proposed Im GCL method. |
| Open Datasets | Yes | Dataset. We use four widely-used datasets including Wiki-CS, Amazon-computers, Amazon-photo, and DBLP, to comprehensively study the performance of transductive node classification. |
| Dataset Splits | Yes | Following (Zhu et al. 2021b), the training set is randomly sampled from the rest according to train/valid/test ratios = 1:1:8, which is highly imbalanced. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'Py GCL (Zhu et al. 2021a) open-source library' but does not provide specific version numbers for any software components, which is required for reproducibility. |
| Experiment Setup | Yes | In Im GCL, we set the number of clusters K in the node centrality based PBS method equal to the number of classes in the downstream task. ... we re-balance the class distribution every B epochs... We select N l nodes during the pre-training phase in Im GCL, where l = 10% equals the ratio of training data in the down-stream task. |