Papers › Spectral Heterogeneous Graph Convolutions via Positive Noncommutative Polynomials
Spectral Heterogeneous Graph Convolutions via Positive Noncommutative Polynomials
Mingguo He, Zhewei Wei, Shikun Feng, Zhengjie Huang, Weibin Li, Yu Sun, dianhai yu
Heterogeneous Graph Neural Networks (HGNNs) have gained significant popularity in various heterogeneous graph learning tasks. However, most existing HGNNs rely on spatial domain-based methods to aggregate information, i.e., manually selected meta-paths or some heuristic modules, lacking theoretical guarantees. Furthermore, these methods cannot learn arbitrary valid heterogeneous graph filters within the spectral domain, which have limited expressiveness. To tackle these issues, we present a positive spectral heterogeneous graph convolution via positive noncommutative polynomials. Then, using this convolution, we propose PSHGCN, a novel Positive Spectral Heterogeneous Graph Convolutional Network. PSHGCN offers a simple yet effective method for learning valid heterogeneous graph filters. Moreover, we demonstrate the rationale of PSHGCN in the graph optimization framework. We conducted an extensive experimental study to show that PSHGCN can learn diverse heterogeneous graph filters and outperform all baselines on open benchmarks. Notably, PSHGCN exhibits remarkable scalability, efficiently handling large real-world graphs comprising millions of nodes and edges. Our codes are available at https://github.com/ivam-he/PSHGCN.
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
1 archive task tag without a task page not shown.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Node Property Prediction | ogbn-mag | PSHGCN (ComplEx embs) | Ext. data | No | #7 of 39 | Archive leaderboard | report |
| Node Property Prediction | ogbn-mag | PSHGCN (ComplEx embs) | Number of params | 4852434 | #7 of 39 | Archive leaderboard | report |
| Node Property Prediction | ogbn-mag | PSHGCN (ComplEx embs) | Test Accuracy | 0.5752 ± 0.0011 | #7 of 39 | Archive leaderboard | report |
| Node Property Prediction | ogbn-mag | PSHGCN (ComplEx embs) | Validation Accuracy | 0.5943 ± 0.0015 | #7 of 39 | Archive leaderboard | report |
| Node Property Prediction | ogbn-mag | PSHGCN | Ext. data | No | #8 of 39 | Archive leaderboard | report |
| Node Property Prediction | ogbn-mag | PSHGCN | Number of params | 4852434 | #8 of 39 | Archive leaderboard | report |
| Node Property Prediction | ogbn-mag | PSHGCN | Test Accuracy | 0.5752 ± 0.0011 | #8 of 39 | Archive leaderboard | report |
| Node Property Prediction | ogbn-mag | PSHGCN | Validation Accuracy | 0.5943 ± 0.0015 | #8 of 39 | 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