Papers › Simple Spectral Graph Convolution
Simple Spectral Graph Convolution
Hao Zhu, Piotr Koniusz
Graph Convolutional Networks (GCNs) have drawn significant attention and become promising methods for learning graph representations. The most GCNs suffer the performance loss when the depth of the model increases. Similarly to CNNs, without specially designed architectures, the performance of a network degrades quickly. Some researchers argue that the required neighbourhood size and neural network depth are two completely orthogonal aspects of graph representation. Thus, several methods extend the neighbourhood by aggregating k-hop neighbourhoods of nodes while using shallow neural networks. However, these methods still encounter oversmoothing, high computation and storage costs. In this paper, we use the Markov diffusion kernel to derive a variant of GCN called Simple Spectral Graph Convolution (S^2GC) which is closely related to spectral models and combines strengths of both spatial and spectral methods. Our spectral analysis shows that our simple spectral graph convolution used in S^2GC is a low-pass filter which partitions networks into a few large parts. Our experimental evaluation demonstrates that S^2GC with a linear learner is competitive in text and node classification tasks. Moreover, S^2GC is comparable to other state-of-the-art methods for node clustering and community prediction tasks.
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 |
|---|---|---|---|---|---|---|---|
| Node Classification | CiteSeer with Public Split: fixed 20 nodes per class | SSGC | Accuracy | 73.6 | #12 of 40 | Archive leaderboard | report |
| Node Classification | Cora: fixed 20 node per class | SSGC | Accuracy | 83.0 | #6 of 9 | Archive leaderboard | report |
| Node Classification | PubMed with Public Split: fixed 20 nodes per class | SSGC | Accuracy | 80.4 | #14 of 37 | Archive leaderboard | report |
| Node Classification | SSGC | Accuracy | 95.3 | #11 of 16 | Archive leaderboard | report | |
| Text Classification | 20NEWS | SSGC | Accuracy | 88.6 | #3 of 16 | Archive leaderboard | report |
| Text Classification | MR | SSGC | Accuracy | 76.7 | #10 of 10 | Archive leaderboard | report |
| Text Classification | Ohsumed | SSGC | Accuracy | 68.5 | #5 of 10 | Archive leaderboard | report |
| Text Classification | R52 | SSGC | Accuracy | 94.5 | #4 of 8 | Archive leaderboard | report |
| Text Classification | R8 | SSGC | Accuracy | 97.4 | #13 of 21 | 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