BG-HGNN: Toward Scalable and Efficient Heterogeneous Graph Neural Network

• URL: http://arxiv.org/abs/2403.08207v1
• Date: Wed, 13 Mar 2024 03:03:40 GMT
• Title: BG-HGNN: Toward Scalable and Efficient Heterogeneous Graph Neural Network
• Authors: Junwei Su, Lingjun Mao, Chuan Wu
• Abstract summary: Heterogeneous graph neural networks (HGNNs) stand out as a promising neural model class designed for heterogeneous graphs. Existing HGNNs employ different parameter spaces to model the varied relationships. This paper introduces Blend&Grind-HGNN, which integrates different relations into a unified feature space manageable by a single set of parameters.
• Score: 6.598758004828656
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many computer vision and machine learning problems are modelled as learning tasks on heterogeneous graphs, featuring a wide array of relations from diverse types of nodes and edges. Heterogeneous graph neural networks (HGNNs) stand out as a promising neural model class designed for heterogeneous graphs. Built on traditional GNNs, existing HGNNs employ different parameter spaces to model the varied relationships. However, the practical effectiveness of existing HGNNs is often limited to simple heterogeneous graphs with few relation types. This paper first highlights and demonstrates that the standard approach employed by existing HGNNs inevitably leads to parameter explosion and relation collapse, making HGNNs less effective or impractical for complex heterogeneous graphs with numerous relation types. To overcome this issue, we introduce a novel framework, Blend&Grind-HGNN (BG-HGNN), which effectively tackles the challenges by carefully integrating different relations into a unified feature space manageable by a single set of parameters. This results in a refined HGNN method that is more efficient and effective in learning from heterogeneous graphs, especially when the number of relations grows. Our empirical studies illustrate that BG-HGNN significantly surpasses existing HGNNs in terms of parameter efficiency (up to 28.96 $\times$), training throughput (up to 8.12 $\times$), and accuracy (up to 1.07 $\times$).

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