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..
Rayleigh Quotient Graph Neural Networks for Graph-level Anomaly Detection
Authors: Xiangyu Dong, Xingyi Zhang, Sibo Wang
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on 10 real-world datasets show that RQGNN outperforms the best rival by 6.74% in Macro-F1 score and 1.44% in AUC, demonstrating the effectiveness of our framework. |
| Researcher Affiliation | Academia | Xiangyu Dong, Xingyi Zhang, Sibo Wang Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong EMAIL |
| Pseudocode | Yes | Algorithm 1: RQL Algorithm 2: CWGNN with RQ-pooling Algorithm 3: RQGNN |
| Open Source Code | Yes | Our code is available at https://github.com/xydong127/RQGNN. |
| Open Datasets | Yes | Datasets. We use 10 real-world datasets to investigate the performance of RQGNN, including MCF7, MOLT-4, PC-3, SW-620, NCI-H23, OVCAR-8, P388, SF-295, SN12C, and UACC257. These datasets are obtained from the TUDataset (Morris et al., 2020), consisting of various chemical compounds and their reactions to different cancer cells. |
| Dataset Splits | Yes | Experimental Settings. We randomly divide each dataset into training/validation/test sets with 70%/15%/15%, respectively. During the sampling process, we ensure that each set maintains a consistent ratio between normal and anomalous graphs. |
| Hardware Specification | No | No specific hardware details (e.g., GPU models, CPU types, memory) used for running the experiments are mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions) are mentioned in the paper. |
| Experiment Setup | Yes | Experimental Settings. We set the learning rate as 0.005, the batch size as 512, the hidden dimension d = 64, the width of CWGNN-RQ q = 4, the depth of CWGNN-RQ K = 6, the dropout rate as 0.4, the hyperparameters of the loss function β = 0.999, γ = 1.5, and we use batch normalization for the final graph embeddings. |