Bridging Graph Neural Networks and Statistical Relational Learning: Relational One-Class GCN

• URL: http://arxiv.org/abs/2102.07007v1
• Date: Sat, 13 Feb 2021 21:34:44 GMT
• Title: Bridging Graph Neural Networks and Statistical Relational Learning: Relational One-Class GCN
• Authors: Devendra Singh Dhami (1), Siwen Yan (2), Sriraam Natarajan (2) ((1) TU Darmstadt, (2) The University of Texas at Dallas)
• Abstract summary: We consider the problem of learning Graph Convolutional Networks (GCNs) for relational data. Our method constructs a secondary graph using relational density estimation techniques.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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