Ensemble Multi-Relational Graph Neural Networks
This work addresses the challenge of designing theoretically grounded GNNs for multi-relational graphs, which is important for domains like knowledge graphs, but it appears incremental as it builds on existing optimization-based GNN frameworks.
The authors tackled the problem of extending graph neural networks (GNNs) to multi-relational graphs by proposing an ensemble multi-relational GNNs based on a new optimization objective, which effectively alleviates over-smoothing and over-parameterization issues, as demonstrated through extensive experiments on four benchmark datasets.
It is well established that graph neural networks (GNNs) can be interpreted and designed from the perspective of optimization objective. With this clear optimization objective, the deduced GNNs architecture has sound theoretical foundation, which is able to flexibly remedy the weakness of GNNs. However, this optimization objective is only proved for GNNs with single-relational graph. Can we infer a new type of GNNs for multi-relational graphs by extending this optimization objective, so as to simultaneously solve the issues in previous multi-relational GNNs, e.g., over-parameterization? In this paper, we propose a novel ensemble multi-relational GNNs by designing an ensemble multi-relational (EMR) optimization objective. This EMR optimization objective is able to derive an iterative updating rule, which can be formalized as an ensemble message passing (EnMP) layer with multi-relations. We further analyze the nice properties of EnMP layer, e.g., the relationship with multi-relational personalized PageRank. Finally, a new multi-relational GNNs which well alleviate the over-smoothing and over-parameterization issues are proposed. Extensive experiments conducted on four benchmark datasets well demonstrate the effectiveness of the proposed model.