论文标题
消息传递超图的神经网络
Message Passing Neural Networks for Hypergraphs
论文作者
论文摘要
HyperGraph表示既更有效，也更适合描述以两个或多个对象之间关系为特征的数据。在这项工作中，我们根据能够处理超图结构数据的消息传递提供了一个新的图神经网络。我们表明，所提出的模型定义了用于超图的神经网络模型的设计空间，从而推广了现有的超图模型。我们在基准数据集上报告了用于节点分类的实验，从而强调了所提出的模型在图形和超图的其他最新方法方面的有效性。我们还讨论了使用HyperGraph表示的好处，同时，在两个以上对象之间存在关系时，突出了使用等效图表示的限制。
Hypergraph representations are both more efficient and better suited to describe data characterized by relations between two or more objects. In this work, we present a new graph neural network based on message passing capable of processing hypergraph-structured data. We show that the proposed model defines a design space for neural network models for hypergraphs, thus generalizing existing models for hypergraphs. We report experiments on a benchmark dataset for node classification, highlighting the effectiveness of the proposed model with respect to other state-of-the-art methods for graphs and hypergraphs. We also discuss the benefits of using hypergraph representations and, at the same time, highlight the limitation of using equivalent graph representations when the underlying problem has relations among more than two objects.
