Efficient Relation-aware Neighborhood Aggregation in Graph Neural Networks via Tensor Decomposition

• URL: http://arxiv.org/abs/2212.05581v4
• Date: Sat, 21 Sep 2024 05:34:50 GMT
• Title: Efficient Relation-aware Neighborhood Aggregation in Graph Neural Networks via Tensor Decomposition
• Authors: Peyman Baghershahi, Reshad Hosseini, Hadi Moradi,
• Abstract summary: We propose a novel knowledge graph that incorporates tensor decomposition within the aggregation function of Graph Conalvolution Network (R-GCN) Our model enhances the representation of neighboring entities by employing projection matrices of a low-rank tensor defined by relation types. We adopt a training strategy inspired by contrastive learning to relieve the training limitation of the 1-k-k encoder method inherent in handling vast graphs.
• Score: 4.041834517339835
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Numerous Graph Neural Networks (GNNs) have been developed to tackle the challenge of Knowledge Graph Embedding (KGE). However, many of these approaches overlook the crucial role of relation information and inadequately integrate it with entity information, resulting in diminished expressive power. In this paper, we propose a novel knowledge graph encoder that incorporates tensor decomposition within the aggregation function of Relational Graph Convolutional Network (R-GCN). Our model enhances the representation of neighboring entities by employing projection matrices of a low-rank tensor defined by relation types. This approach facilitates multi-task learning, thereby generating relation-aware representations. Furthermore, we introduce a low-rank estimation technique for the core tensor through CP decomposition, which effectively compresses and regularizes our model. We adopt a training strategy inspired by contrastive learning, which relieves the training limitation of the 1-N method inherent in handling vast graphs. We outperformed all our competitors on two common benchmark datasets, FB15k-237 and WN18RR, while using low-dimensional embeddings for entities and relations.

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