[论文解读] Edge Attention-based Multi-Relational Graph Convolutional Networks

Chao Shang, Qinqing Liu+5 more|arXiv (Cornell University)|Feb 14, 2018

Computational Drug Discovery Methods参考文献 34被引用 68

一句话总结

本文提出 EAGCN，一种能够处理具有多种边属性的图的 GCN，通过为每种键属性学习边注意力矩阵并聚合节点特征来进行分子性质预测。

ABSTRACT

Graph convolutional network (GCN) is generalization of convolutional neural network (CNN) to work with arbitrarily structured graphs. A binary adjacency matrix is commonly used in training a GCN. Recently, the attention mechanism allows the network to learn a dynamic and adaptive aggregation of the neighborhood. We propose a new GCN model on the graphs where edges are characterized in multiple views or precisely in terms of multiple relationships. For instance, in chemical graph theory, compound structures are often represented by the hydrogen-depleted molecular graph where nodes correspond to atoms and edges correspond to chemical bonds. Multiple attributes can be important to characterize chemical bonds, such as atom pair (the types of atoms that a bond connects), aromaticity, and whether a bond is in a ring. The different attributes lead to different graph representations for the same molecule. There is growing interests in both chemistry and machine learning fields to directly learn molecular properties of compounds from the molecular graph, instead of from fingerprints predefined by chemists. The proposed GCN model, which we call edge attention-based multi-relational GCN (EAGCN), jointly learns attention weights and node features in graph convolution. For each bond attribute, a real-valued attention matrix is used to replace the binary adjacency matrix. By designing a dictionary for the edge attention, and forming the attention matrix of each molecule by looking up the dictionary, the EAGCN exploits correspondence between bonds in different molecules. The prediction of compound properties is based on the aggregated node features, which is independent of the varying molecule (graph) size. We demonstrate the efficacy of the EAGCN on multiple chemical datasets: Tox21, HIV, Freesolv, and Lipophilicity, and interpret the resultant attention weights.

研究动机与目标

• 直接从具有多种键属性的分子图中学习分子性质的动机。
• 提出一个变体的 GCN，使用边特定注意力矩阵而非二元邻接矩阵。
• 通过使用字典在键属性和分子之间共享边注意力来实现跨分子的一致性。
• 在标准化学数据集上演示模型的有效性。
• 解释学习到的注意力权重，以揭示键的重要性。

提出的方法

• 为每个键属性引入边注意力矩阵，以取代二元邻接矩阵。
• 使用字典来映射边的注意力，使不同分子之间能够建立对应关系。
• 在图卷积中联合学习注意力权重和节点特征。
• 聚合节点特征以产生独立于图大小的分子级预测。
• 在化学数据集上进行评估以验证性能并解释注意力。

实验结果

研究问题

• RQ1 对于多种键属性的边级注意力，能否比二元邻接 GCN 提高分子性质预测？
• RQ2 通过字典在分子间共享边注意力对学习和泛化有何影响？
• RQ3 学习到的注意力权重是否能提供关于性质相关键的可解释见解？
• RQ4 EAGCN 是否能够在无需手工指纹的情况下扩展到具有变化大小和属性的图？

主要发现

• EAGCN 在化学数据集 Tox21、HIV、Freesolv 和 Lipophilicity 上显示出有效性。
• 该模型在多关系图上联合学习注意力权重与节点特征。
• 注意力权重可被解释以理解预测中的键的重要性。
• 边注意力方法实现了跨多种键属性的动态自适应聚合。