Spectro-Riemannian Graph Neural Networks
Authors: Karish Grover, Haiyang Yu, Xiang song, Qi Zhu, Han Xie, Vassilis Ioannidis, Christos Faloutsos
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evaluation across eight homophilic and heterophilic datasets demonstrates the superiority of CUSP in node classification and link prediction tasks, with a gain of up to 5.3% over state-of-the-art models. |
| Researcher Affiliation | Collaboration | 1Carnegie Mellon University, 2Texas A&M University, 3Amazon EMAIL, {haiyang}@tamu.edu, EMAIL |
| Pseudocode | Yes | Algorithm 1 Product manifold signature estimation and curvature initialisation |
| Open Source Code | Yes | The code is available at: https://github.com/amazon-science/cusp. |
| Open Datasets | Yes | We evaluate CUSP on the Node Classification (NC) and Link Prediction (LP) tasks using eight benchmark datasets. These include (a) Homophilic datasets such as (i) Citation networks Cora, Citeseer and Pub Med (Sen et al., 2008; Yang et al., 2016), and (b) Heterophilic datasets, which comprise (i) Wikipedia graphs Chameleon and Squirrel (Rozemberczki et al., 2021), (ii) Actor co-occurrence network (Tang et al., 2009), and (iii) Webpage graphs from Web KB 2 Texas and Cornell. |
| Dataset Splits | Yes | For the transductive LP task, we randomly split edges into 85%/5%/10% for training, validation and test sets, while for transductive NC task, we use the 60%/20%/20% split. |
| Hardware Specification | No | The paper does not explicitly mention specific hardware details like GPU/CPU models, processor types, or memory amounts used for running its experiments. The 'MORE EXPERIMENTAL SETTINGS' section (Appendix 7.6.5) primarily focuses on hyperparameters. |
| Software Dependencies | No | The paper mentions using the 'Geoopt library' but does not specify a version number. It also refers to algorithms like 'Sinkhorn algorithm' and 'Hungarian algorithm' but not specific software libraries with version numbers. |
| Experiment Setup | Yes | For all experiments, we choose the total manifold dimension as d M = 48 and learning rate as 4e-3. We use the filter bank ΩPd M = ZI Pd M, Z(1) Pd M, Z(2) Pd M, . . . , Z(L) Pd M , with L = 10. For the GPR weights, we experiment with different initializations, α {0.1, 0.3, 0.5, 0.9}. We list the hyperparameter settings in Appendix 7.6.5. |