论文标题
使均匀的GNN通过关系嵌入处理异质图
Enabling Homogeneous GNNs to Handle Heterogeneous Graphs via Relation Embedding
论文作者
论文摘要
图形神经网络（GNN）已被推广以通过各种方法处理异质图。不幸的是，这些方法通常通过各种复杂的模块对异质性进行建模。本文旨在提出一个简单而有效的框架，以将足够的能力分配给同质GNN来处理异质图。具体而言，我们提出了基于关系嵌入的图形神经网络（RE-GNN），该图仅使用一个参数，每一个关系嵌入了不同类型的关系和节点型特异性自动循环连接的重要性。为了同时优化这些关系嵌入和模型参数，提出了一个梯度缩放系数来约束嵌入以收敛到合适的值。此外，我们从两个角度解释了所提出的RE-GNN，从理论上讲，我们的RE-GCN具有比GTN更具表现力的能力（这是一种典型的异质GNN，并且可以自适应地产生元数据）。广泛的实验表明，我们的RE-GNN可以有效，有效地处理异质图，并可以应用于各种均匀的GNN。
Graph Neural Networks (GNNs) have been generalized to process the heterogeneous graphs by various approaches. Unfortunately, these approaches usually model the heterogeneity via various complicated modules. This paper aims to propose a simple yet effective framework to assign adequate ability to the homogeneous GNNs to handle the heterogeneous graphs. Specifically, we propose Relation Embedding based Graph Neural Network (RE-GNN), which employs only one parameter per relation to embed the importance of distinct types of relations and node-type-specific self-loop connections. To optimize these relation embeddings and the model parameters simultaneously, a gradient scaling factor is proposed to constrain the embeddings to converge to suitable values. Besides, we interpret the proposed RE-GNN from two perspectives, and theoretically demonstrate that our RE-GCN possesses more expressive power than GTN (which is a typical heterogeneous GNN, and it can generate meta-paths adaptively). Extensive experiments demonstrate that our RE-GNN can effectively and efficiently handle the heterogeneous graphs and can be applied to various homogeneous GNNs.
