A General Graph Spectral Wavelet Convolution via Chebyshev Order Decomposition
Authors: Nian Liu, Xiaoxin He, Thomas Laurent, Francesco Di Giovanni, Michael M. Bronstein, Xavier Bresson
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our numerical experiments showcase the consistent improvements in both short-range and long-range tasks. This underscores the effectiveness of the proposed model in handling different scenarios. Our code is available at https: //github.com/liun-online/Wave GC. |
| Researcher Affiliation | Collaboration | 1National University of Singapore 2Loyola Marymount University 3University of Oxford 4AITHYRA, Austria. Correspondence to: Nian Liu <EMAIL>. |
| Pseudocode | No | The paper describes methods using mathematical formulations and textual explanations but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https: //github.com/liun-online/Wave GC. |
| Open Datasets | Yes | Datasets for short-range tasks: CS, Photo, Computer and Cora Full from the Py Torch Geometric (Py G) (Fey & Lenssen, 2019), and one large-size graph, i.e. ogbn-arxiv from Open Graph Benchmark (OGB) (Hu et al., 2020) (2) Datasets for long-range tasks: Pascal VOC-SP (VOC), PCQM-Contact (PCQM), COCO-SP (COCO), Peptides-func (Pf) and Peptides-struct (Ps) from LRGB (Dwivedi et al., 2022). |
| Dataset Splits | Yes | For short-range (S) datasets, we follow the settings from (Chen et al., 2022). For ogbn-arxiv, we use the public splits in OGB (Hu et al., 2020). For longrange datasets, we adhere to the experimental configurations outlined in (Dwivedi et al., 2022). |
| Hardware Specification | Yes | GPU information: NVIDIA RTX A5000 |
| Software Dependencies | No | We implement our Wave GC in Py Torch, and list some important parameter values in our model in Table 13. Please note that for the five long-range datasets, we follow the parameter budget 500k (Dwivedi et al., 2022). The paper mentions Py Torch but does not provide a specific version number for it or any other key software libraries. |
| Experiment Setup | Yes | Table 13. The values of parameters used in Wave GC (T: True; F: False). Dataset # parameters ρ J s Tight frames ℵ CS 495k 3 3 {0.5, 0.5, 0.5} T 0.1 Photo 136k 3 3 {1.0, 1.0, 1.0} T 0.1 Computer 167k 7 3 {10.0, 10.0, 10.0} T 0.1 Cora Full 621k 3 3 {2.0, 2.0, 2.0} T 0.1 ogbn-arxiv 2,354k 3 3 {5.0, 5.0, 5.0} F / Pascal VOC-SP 506k 5 3 {0.5, 1.0, 10.0} T / PCQM-Contact 508k 5 3 {0.5, 1.0, 5.0} T / COCO-SP 546k 3 3 {0.5, 1.0, 10.0} T / Peptides-func 496k 5 3 {10.0, 10.0, 10.0} T / Peptides-struct 534k 3 3 {10.0, 10.0, 10.0} F / |