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Composition-based Multi-Relational Graph Convolutional Networks
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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.
Forward citations
Cited by 7 Pith papers
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Context-Driven Knowledge Graph Completion with Semantic-Aware Relational Message Passing
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Sequential Learner Modeling Using Multi-Relational Graph Convolutional Networks
An unsupervised multi-relational GCN learner-modeling pipeline is described, but its own user study finds no significant benefit over the single-relation ConceptGCN baseline.
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Unifying Adversarial Perturbation for Graph Neural Networks
Adding perturbations directly to every hidden embedding of a GNN is claimed to subsume existing feature-, edge-, and weight-perturbation defenses, but the claim rests on simplifications that the experiments do not act...
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