Papers › Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks
Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks
Muhammet Balcilar, Guillaume Renton, Pierre Heroux, Benoit Gauzere, Sebastien Adam, Paul Honeine
This paper aims at revisiting Graph Convolutional Neural Networks by bridging the gap between spectral and spatial design of graph convolutions. We theoretically demonstrate some equivalence of the graph convolution process regardless it is designed in the spatial or the spectral domain. The obtained general framework allows to lead a spectral analysis of the most popular ConvGNNs, explaining their performance and showing their limits. Moreover, the proposed framework is used to design new convolutions in spectral domain with a custom frequency profile while applying them in the spatial domain. We also propose a generalization of the depthwise separable convolution framework for graph convolutional networks, what allows to decrease the total number of trainable parameters by keeping the capacity of the model. To the best of our knowledge, such a framework has never been used in the GNNs literature. Our proposals are evaluated on both transductive and inductive graph learning problems. Obtained results show the relevance of the proposed method and provide one of the first experimental evidence of transferability of spectral filter coefficients from one graph to another. Our source codes are publicly available at: https://github.com/balcilar/Spectral-Designed-Graph-Convolutions
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Graph Classification | ENZYMES | DSGCN-allfeat | Accuracy | 78.39 | #4 of 54 | Archive leaderboard | report |
| Graph Classification | ENZYMES | DSGCN-nodelabel | Accuracy | 65.13 | #21 of 54 | Archive leaderboard | report |
| Node Classification | CiteSeer with Public Split: fixed 20 nodes per class | DSGCN | Accuracy | 73.3 | #17 of 40 | Archive leaderboard | report |
| Node Classification | Cora with Public Split: fixed 20 nodes per class | DSGCN | Accuracy | 84.2% | #12 of 36 | Archive leaderboard | report |
| Node Classification | Cora: fixed 20 node per class | DSGCN | Accuracy | 84.2 | #1 of 9 | Archive leaderboard | report |
| Node Classification | PPI | DSGCN | F1 | 99.09 ± 0.03 | #11 of 24 | Archive leaderboard | report |
| Node Classification | PubMed with Public Split: fixed 20 nodes per class | DSGCN | Accuracy | 81.9% | #5 of 37 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections