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..
Cross-Domain Graph Anomaly Detection via Anomaly-Aware Contrastive Alignment
Authors: Qizhou Wang, Guansong Pang, Mahsa Salehi, Wray Buntine, Christopher Leckie
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on eight CD-GAD settings demonstrate that our approach ACT achieves substantially improved detection performance over 10 state-of-the-art GAD methods. |
| Researcher Affiliation | Academia | 1 Monash University 2 Singapore Management University 3 Vin University 4 The University of Melbourne |
| Pseudocode | No | The paper describes its method in text and equations but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/QZ-WANG/ACT. |
| Open Datasets | Yes | Eight CD-GAD settings based on four real-world GAD datasets, including Yelp Hotel (HTL), Yelp Res (RES), Yelp NYC (NYC) and Amazon (AMZ)1, are created... 1Statistics of each dataset are given in Suppl. Material |
| Dataset Splits | No | The paper describes the datasets used (Yelp Hotel, Yelp Res, Yelp NYC, Amazon) and states '1Statistics of each dataset are given in Suppl. Material' but does not provide specific train/validation/test split percentages or sample counts in the main text. |
| Hardware Specification | No | The paper mentions running experiments on the 'MASSIVE HPC facility', but does not provide specific details such as GPU or CPU models, or other hardware specifications. |
| Software Dependencies | No | The paper mentions the use of 'Graph SAGE' and 'ADAM optimiser' but does not specify version numbers for any software dependencies. |
| Experiment Setup | Yes | Our model ACT is implemented with a three-layer Graph SAGE ... 256 and 64 hidden dimensions are chosen for ψs and ψt respectively. The source model is trained for 50 epochs using a learning rate of 10 3. The domain alignment is performed for 50 epochs using a learning rate of 10 4. ... The optimisation is done in mini-batches of 128 target (centre) nodes using the ADAM optimiser... We use the sample size of 25 and 10 for the two hidden layers during message passing. In self labelling, α = 2.5 and q = 25 are used by default. |