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 Contrastive Graph Masked AutoEncoder Against Graph Structure and Feature Dual Attacks
Authors: Weixuan Shen, Xiaobo Shen, Shirui Pan
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on node classification and clustering tasks demonstrate the effectiveness of the proposed ACGMAE, especially under graph structure and feature dual attacks. |
| Researcher Affiliation | Academia | 1Nanjing University of Science and Technology, Nanjing, China 2Griffith University, Gold Coast, Australia |
| Pseudocode | Yes | Algorithm 1: Algorithm of ACGMAE |
| Open Source Code | No | The paper does not contain an explicit statement about releasing the source code for the methodology described, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We evaluate ACGMAE and baselines on three benchmark datasets, i.e., Cora, Citeseer, Pubmed (Jin et al. 2020) |
| Dataset Splits | Yes | For node classification, we randomly select 10% nodes for training, 10% nodes for validation, and the remaining for testing. |
| Hardware Specification | Yes | The experiments are performed on a Ubuntu Enterprise 64Bit Linux workstation with 128G memory and a NVIDIA A6000 GPU server. |
| Software Dependencies | No | The paper mentions that a two-layer GCN is employed as the encoder, but it does not specify any software names with version numbers (e.g., Python, PyTorch, TensorFlow, or specific libraries). |
| Experiment Setup | Yes | In the proposed ACGMAE, the learning rate and weight decay are searched from {0.01, 0.001, 0.0001} and {0.0001, 0.0005, 0.0001, 0.00005} respectively. The perturbation ratio X is searched from {0.1, 0.3, 0.5, 0.7, 0.9}, and the number of nearest neighbors and the number of clusters are searched from {10, 15, 20, 25, 30}. The coefficients α, β, and γ are searched from {0.01, 0.1, 0.5, 1, 3, 5}. |