论文标题
合奏多关系图神经网络
Ensemble Multi-Relational Graph Neural Networks
论文作者
论文摘要
众所周知，可以从优化目标的角度来解释和设计图形神经网络（GNN）。有了这个明确的优化目标，推论的GNNS体系结构具有合理的理论基础，能够灵活地纠正GNN的弱点。但是，仅对具有单相关图的GNN证明了此优化目标。我们是否可以通过扩展此优化目标来推断一种新型的GNN用于多关系图，以便同时解决以前的多关系GNN中的问题，例如过度参数化？在本文中，我们通过设计一个集合多关系（EMR）优化目标提出了一种新型的集合多关系GNN。此EMR优化目标能够得出一个迭代更新规则，该规则可以正式化为具有多关系的整体消息传递（ENMP）层。我们进一步分析了ENMP层的良好属性，例如，与多关系个性化的Pagerank的关系。最后，提出了一种新的跨关系GNN，可以很好地缓解过度平滑和过度参数化问题。在四个基准数据集上进行的广泛实验很好地证明了该模型的有效性。
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.
