Efficient Heterogeneous Graph Learning via Random Projection

IF 8.9 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jun Hu;Bryan Hooi;Bingsheng He
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引用次数: 0

Abstract

Heterogeneous Graph Neural Networks (HGNNs) are powerful tools for deep learning on heterogeneous graphs. Typical HGNNs require repetitive message passing during training, limiting efficiency for large-scale real-world graphs. Recent pre-computation-based HGNNs use one-time message passing to transform a heterogeneous graph into regular-shaped tensors, enabling efficient mini-batch training. Existing pre-computation-based HGNNs can be mainly categorized into two styles, which differ in how much information loss is allowed and efficiency. We propose a hybrid pre-computation-based HGNN, named Random Projection Heterogeneous Graph Neural Network (RpHGNN), which combines the benefits of one style's efficiency with the low information loss of the other style. To achieve efficiency, the main framework of RpHGNN consists of propagate-then-update iterations, where we introduce a Random Projection Squashing step to ensure that complexity increases only linearly. To achieve low information loss, we introduce a Relation-wise Neighbor Collection component with an Even-odd Propagation Scheme, which aims to collect information from neighbors in a finer-grained way. Experimental results indicate that our approach achieves state-of-the-art results on seven small and large benchmark datasets while also being 230% faster compared to the most effective baseline. Surprisingly, our approach not only surpasses pre-processing-based baselines but also outperforms end-to-end methods.
通过随机投影实现高效异构图学习
异构图神经网络(HGNN)是在异构图上进行深度学习的强大工具。典型的 HGNNs 在训练过程中需要重复传递信息,限制了大规模真实图的效率。最近推出的基于预计算的 HGNNs 使用一次性消息传递将异构图转换为规则形状的张量,从而实现了高效的小型批量训练。现有的基于预计算的 HGNNs 主要分为两种类型,它们在允许信息丢失的程度和效率上有所不同。我们提出了一种基于预计算的混合 HGNN,命名为随机投影异构图神经网络(RpHGNN),它结合了一种 HGNN 的高效性和另一种 HGNN 的低信息丢失性。为了实现高效,RpHGNN 的主要框架包括传播-更新迭代,我们引入了随机投影挤压步骤,以确保复杂度仅线性增长。为了实现低信息损失,我们引入了一个具有偶数传播方案的 "关系-明智-邻居收集 "组件,旨在以更精细的方式收集邻居信息。实验结果表明,我们的方法在七个小型和大型基准数据集上取得了最先进的结果,与最有效的基线相比,速度还提高了 230%。令人惊讶的是,我们的方法不仅超越了基于预处理的基线方法,而且还优于端到端方法。
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来源期刊
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering 工程技术-工程:电子与电气
CiteScore
11.70
自引率
3.40%
发文量
515
审稿时长
6 months
期刊介绍: The IEEE Transactions on Knowledge and Data Engineering encompasses knowledge and data engineering aspects within computer science, artificial intelligence, electrical engineering, computer engineering, and related fields. It provides an interdisciplinary platform for disseminating new developments in knowledge and data engineering and explores the practicality of these concepts in both hardware and software. Specific areas covered include knowledge-based and expert systems, AI techniques for knowledge and data management, tools, and methodologies, distributed processing, real-time systems, architectures, data management practices, database design, query languages, security, fault tolerance, statistical databases, algorithms, performance evaluation, and applications.
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