Graph Convolutional Networks (GCNs) have recently been shown to be quite
successful in modeling graph-structured data. However, the primary focus has
been on handling simple undirected graphs. Multi-relational graphs are a more
general and prevalent form of graphs where each edge has a label and direction
associated with it. Most of the existing approaches to handle such graphs
suffer from over-parameterization and are restricted to learning
representations of nodes only. In this paper, we propose CompGCN, a novel Graph
Convolutional framework which jointly embeds both nodes and relations in a
relational graph. CompGCN leverages a variety of entity-relation composition
operations from Knowledge Graph Embedding techniques and scales with the number
of relations. It also generalizes several of the existing multi-relational GCN
methods. We evaluate our proposed method on multiple tasks such as node
classification, link prediction, and graph classification, and achieve
demonstrably superior results. We make the source code of CompGCN available to
foster reproducible research.

本研究提出了 CompGCN，一种新的图卷积框架，可同时嵌入节点和关系，解决了处理多关系图的过度参数化问题，广义了现有的多关系 GCN 方法，取得了节点分类、链接预测和图分类等多个任务的显着优异结果。