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 [1].
CurvGAD: Leveraging Curvature for Enhanced Graph Anomaly Detection
Authors: Karish Grover, Geoffrey J. Gordon, Christos Faloutsos
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimentation over 10 real-world datasets (both homophilic and heterophilic) demonstrates an improvement of up to 6.5% over state-of-the-art GAD methods. The code is available at: https://github.com/ karish-grover/curvgad. |
| Researcher Affiliation | Academia | Karish Grover 1 Geoffrey J. Gordon 1 Christos Faloutsos 1 1Carnegie Mellon University. Correspondence to: Karish Grover <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Discrete Ollivier-Ricci Flow Algorithm 2 Product manifold signature estimation |
| Open Source Code | Yes | The code is available at: https://github.com/ karish-grover/curvgad. |
| Open Datasets | Yes | We evaluate Curv GAD on 10 datasets, each containing organic node-level anomalies, to assess its effectiveness across homophilic and heterophilic settings. These datasets span multiple domains, including social media, e-commerce, and financial networks. Specifically, (1) Weibo (Zhao et al., 2020; Liu et al., 2022), (2) Reddit (Kumar et al., 2019; Liu et al., 2022), (3) Questions (Platonov et al., 2023), and (4) T-Social (Tang et al., 2022)... |
| Dataset Splits | Yes | If splits are not provided, we adopt the strategy from (Tang et al., 2022), partitioning nodes into 40%/20%/40% for training, validation, and testing, as detailed in Table 6. To ensure robustness, we perform ten random splits per dataset and report the average performance. |
| Hardware Specification | Yes | All experiments are conducted on A6000 GPUs (48GB), using a total manifold dimension of d P = 48, a learning rate of 0.01, and a filterbank comprising F = 8 filters. |
| Software Dependencies | No | The paper mentions: "we leverage the Îș-stereographic product manifold implementation from Geoopt1 and use Riemannian Adam for gradient-based learning across product manifolds." and footnote 1: "https://github.com/geoopt/geoopt". However, specific version numbers for Geoopt or other software dependencies are not provided. |
| Experiment Setup | Yes | All experiments are conducted on A6000 GPUs (48GB), using a total manifold dimension of d P = 48, a learning rate of 0.01, and a filterbank comprising F = 8 filters. Appendix E.2 enlists the hyperparameter configurations tried and we analyse the time complexity of Curv GAD in Appendix C. Table 7: Hyperparameter Tuning Configurations Description F {3, 5, 8, 10, 20, 25} Total number of graph filters. ... lr {1e 4, 2e 3, 0.001, 0.01} Learning rate weight decay {0, 1e 4, 5e 4} Weight decay |