Computer Science > Machine Learning
[Submitted on 31 May 2023 (v1), last revised 7 May 2024 (this version, v3)]
Title:Spectral Heterogeneous Graph Convolutions via Positive Noncommutative Polynomials
View PDF HTML (experimental)Abstract: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 this https URL.
Submission history
From: Mingguo He [view email][v1] Wed, 31 May 2023 14:09:42 UTC (66 KB)
[v2] Sun, 5 May 2024 08:45:00 UTC (663 KB)
[v3] Tue, 7 May 2024 02:20:18 UTC (663 KB)
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