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..
GOODAT: Towards Test-Time Graph Out-of-Distribution Detection
Authors: Luzhi Wang, Dongxiao He, He Zhang, Yixin Liu, Wenjie Wang, Shirui Pan, Di Jin, Tat-Seng Chua
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive evaluations confirm that our GOODAT method outperforms state-of-the-art benchmarks across a variety of real-world datasets. |
| Researcher Affiliation | Academia | 1College of Intelligence and Computing, Tianjin University 2Faculty of Information Technology, Monash University 3School of Computing, National University of Singapore 4School of Information and Communication Technology, Griffith University |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about open-sourcing the code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | The graph OOD detection benchmark contains 8 pairs of molecule datasets, 1 pair of bioinformatics datasets, and 1 pair of social network datasets. ... The graph anomaly detection benchmark comprises 15 datasets from TU benchmark (Morris et al. 2020). |
| Dataset Splits | No | The paper mentions a 90% training and 10% test split, but does not explicitly provide details for a separate validation split or how it was handled. |
| Hardware Specification | Yes | Experiments run on a GeForce GTX TITAN X GPU with 24 GB memory |
| Software Dependencies | No | The paper mentions using the Adam optimizer but does not specify version numbers for any software components or libraries. |
| Experiment Setup | Yes | We use the Adam optimizer (Kingma and Ba 2014) for optimization. ... We conduct a parameter sensitivity experiment on the PTC-MR/MUTAG dataset, where α is selected from {0.1, 0.3, 0.5, 0.7, 0.9} and β is selected from {0.01, 0.03, 0.05, 0.07, 0.09}. As shown in Fig. 5 (a), when β is fixed, optimal outcomes are achieved with α in the range of 0.1-0.3. Likewise, when α is held constant, β values in the range of 0.3-0.5 yield optimal results... |