Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Certifiably Robust Graph Contrastive Learning
Authors: Minhua Lin, Teng Xiao, Enyan Dai, Xiang Zhang, Suhang Wang
NeurIPS 2023 | Venue PDF | LLM Run Details | Input Tokens: 35,343 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,325 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on real-world datasets demonstrate the effectiveness of our proposed method in providing effective certifiable robustness and enhancing the robustness of any GCL model. |
| Researcher Affiliation | Academia | Minhua Lin, Teng Xiao, Enyan Dai, Xiang Zhang, Suhang Wang The Pennsylvania State University EMAIL |
| Pseudocode | Yes | Algorithm 1 The Training Algorithm of RES. |
| Open Source Code | Yes | The source code of RES is available at https://github.com/ventr1c/RES-GCL. |
| Open Datasets | Yes | We conduct experiments on 4 public benchmark datasets for node classification, i.e., Cora, Pubmed [47], Coauthor-Physics [48] and OGB-arxiv [49], and 3 widely used dataset for graph classification i.e., MUTAG, PROTEINS [50] and OGB-molhiv [49]. |
| Dataset Splits | Yes | We use public splits for Cora and Pubmed, and for five other datasets, we perform a 10/10/80 random split for training, validation, and testing, respectively. |
| Hardware Specification | Yes | All models are trained on an A6000 GPU with 48G memory. |
| Software Dependencies | No | The paper mentions 'Py GCL library [65]' and 'GCN' but does not specify version numbers for these software dependencies or other key libraries. |
| Experiment Setup | No | The paper mentions 'A 2-layer GCN is employed as the backbone GNN encoder' and 'All hyperparameters of the baselines are tuned based on the validation set', but it does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) in the main text. |