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..
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 | 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 / |