Core Concepts
The proposed Positive Spectral Heterogeneous Graph Convolutional Network (PSHGCN) enables effective learning of diverse spectral heterogeneous graph convolutions by utilizing positive noncommutative polynomials.
Abstract
The paper introduces PSHGCN, a novel heterogeneous convolutional network that extends spectral-based graph neural networks (GNNs) to heterogeneous graphs. Key highlights:
Existing heterogeneous graph neural networks (HGNNs) primarily focus on spatial domain-based message passing and attention modules, neglecting the utilization of spectral graph convolutions.
PSHGCN proposes a simple yet effective approach for learning spectral heterogeneous graph convolutions by employing positive noncommutative polynomials. This ensures the learned convolutions are positive semidefinite, a key requirement for valid graph convolutions.
The authors establish a generalized heterogeneous graph optimization framework and demonstrate the rationale of PSHGCN within this framework. PSHGCN represents the essential structure of any valid heterogeneous convolution.
Extensive experiments show that PSHGCN can learn diverse spectral heterogeneous graph convolutions and achieve superior performance in node classification tasks, including on the large-scale ogbn-mag dataset, highlighting its scalability.
PSHGCN is the first model that attempts to learn polynomial spectral graph convolutions on heterogeneous graphs, opening up new research directions in this area.
Stats
The paper does not provide specific numerical data or statistics to support the key logics. The focus is on the theoretical framework and model design.
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