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..
Towards Self-Interpretable Graph-Level Anomaly Detection
Authors: Yixin Liu, Kaize Ding, Qinghua Lu, Fuyi Li, Leo Yu Zhang, Shirui Pan
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on 16 datasets demonstrate the anomaly detection capability and self-interpretability of SIGNET. |
| Researcher Affiliation | Academia | 1Monash University, 2Northwestern University, 3Data61, CSIRO, 4Northwest A&F University, 5The University of Adelaide, 6Griffith University |
| Pseudocode | Yes | More discussion about methodology, including the pseudo-code algorithm of SIGNET, the comparison between SIGNET and existing method, and the complexity analysis of SIGNET, is illustrated in Appendix E. |
| Open Source Code | Yes | Our code is available at https://github.com/yixinliu233/SIGNET. |
| Open Datasets | Yes | We also verify the anomaly detection performance of SIGNET on 10 TU datasets [58], following the setting in [4]. ... MNIST-0 and MNIST-1 are two GLAD datasets derived from MNIST-75sp superpixel dataset [59]. ... MUTAG is a molecular property prediction dataset [61]. |
| Dataset Splits | No | Given the training set Gtr that contains a number of normal graphs, we aim at learning an explainable GLAD model f : G (R, G) that is able to predict the abnormality of a graph and provide corresponding explanations. In specific, given a graph Gi from the test set Gte with normal and abnormal graphs... It mentions "training set" and "test set" but does not explicitly provide validation split percentages or counts. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models or detailed computer specifications used for running experiments. |
| Software Dependencies | No | In SIGNET, we use GIN [2] and Hyper-Conv [30] as the GNN and HGNN encoders. The paper names software components but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | For all methods, we perform 5 random runs and report the average performance. We use Adam optimizer with learning rate 0.001 and weight decay 0.0001. We train 100 epochs with early stopping (patience 20). We use a batch size of 128. For SIGNET, the GNN encoder (GIN) and HGNN encoder (Hyper-Conv) are 2-layer, and the hidden dimension is 64. The bottleneck subgraph extractor (MLP) has 2 layers. We set the temperature parameter τ = 0.5. The number of negative samples is 128 for Info-NCE. The trade-off parameter β = 0.001. |