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..
Adversarial Graph Augmentation to Improve Graph Contrastive Learning
Authors: Susheel Suresh, Pan Li, Cong Hao, Jennifer Neville
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally validate AD-GCL2 by comparing with the state-of-the-art GCL methods and achieve performance gains of up-to 14% in unsupervised, 6% in transfer, and 3% in semi-supervised learning settings overall with 18 different benchmark datasets for the tasks of molecule property regression and classification, and social network classification. |
| Researcher Affiliation | Collaboration | Susheel Suresh Purdue University EMAIL Pan Li Purdue University EMAIL Cong Hao Georgia Tech EMAIL Jennifer Neville Purdue University and Microsoft Research EMAIL |
| Pseudocode | Yes | (See Appendix D for a summary of AD-GCL in its algorithmic form.) |
| Open Source Code | Yes | 2https://github.com/susheels/adgcl |
| Open Datasets | Yes | We use datasets from Open Graph Benchmark (OGB) [52], TU Dataset [73] and ZINC [74] for graph-level property classification and regression. |
| Dataset Splits | Yes | AD-GCL-OPT assumes the augmentation search space has some weak information from the downstream task. A full range of analysis on how λreg impacts AD-GCL will be investigated in Sec. 5.2. We compare AD-GCL with three unsupervised/selfsupervised learning baselines for graph-level tasks, which include randomly initialized untrained GIN (RU-GIN) [72], Info Graph [18] and Graph CL [24]. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions using GIN as the encoder and implies standard machine learning frameworks but does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | We consider two types of AD-GCL, where one is with a fixed regularization weight λreg = 5 (Eq.8), termed AD-GCL-FIX, and another is with λreg tuned over the validation set among {0.1, 0.3, 0.5, 1.0, 2.0, 5.0, 10.0}, termed AD-GCL-OPT. |