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 | Conference PDF | Archive PDF | Plain Text | 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... |