Ensemble Multi-Relational Graph Neural Networks
Ensemble Multi-Relational Graph Neural Networks
Yuling Wang, Hao Xu, Yanhua Yu, Mengdi Zhang, Zhenhao Li, Yuji Yang, Wei Wu
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
Main Track. Pages 2298-2304.
https://doi.org/10.24963/ijcai.2022/319
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.
Keywords:
Data Mining: Mining Graphs
Data Mining: Mining Heterogenous Data
Machine Learning: Representation learning
Machine Learning: Semi-Supervised Learning