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تسجيل مستخدم جديدBridging Graph Neural Networks and Statistical Relational Learning: Relational One-Class GCN
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We consider the problem of learning Graph Convolutional Networks (GCNs) for relational data. Specifically, we consider the classic link prediction and node classification problems as relational modeling tasks and develop a relational extension to GCNs. Our method constructs a secondary graph using relational density estimation techniques where vertices correspond to the target triples. We emphasize the importance of learning features using the secondary graph and the advantages of employing a distance matrix over the typically used adjacency matrix. Our comprehensive empirical evaluation demonstrates the superiority of our approach over $mathbf{12}$ different GCN models, relational embedding techniques, rule learning techniques and relational models.
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