In this work, we introduce a hypergraph representation learning framework called Hypergraph Neural Networks (HNN) that jointly learns hyperedge embeddings along with a set of hyperedge-dependent embeddings for each node in the hypergraph. HNN derives multiple embeddings per node in the hypergraph where each embedding for a node is dependent on a specific hyperedge of that node. Notably, HNN is accurate, data-efficient, flexible with many interchangeable components, and useful for a wide range of hypergraph learning tasks. We evaluate the effectiveness of the HNN framework for hyperedge prediction and hypergraph node classification. We find that HNN achieves an overall mean gain of 7.72% and 11.37% across all baseline models and graphs for hyperedge prediction and hypergraph node classification, respectively.
翻译:在这项工作中,我们引入了一个称为高射神经网络(HNN)的高射代表学习框架(HNN),共同学习高射层嵌入,同时为高射线中每个节点学习一套高射基嵌入。HNN在高射线中产生每个节点的多个嵌入。每个结点的嵌入都取决于该节点的特定顶端。值得注意的是,HNN是准确、数据高效、灵活且有许多可互换的组件,并可用于广泛的高射线学习任务。我们评估了HNN框架在高射预测和高射节点分类方面的有效性。我们发现,HNN在所有高射线预测和高射线分类基线模型和图表中,其总体平均收益分别为7.72%和11.37 % 。