论文标题
一个用于学习超边缘依赖性节点嵌入的超图神经网络框架
A Hypergraph Neural Network Framework for Learning Hyperedge-Dependent Node Embeddings
论文作者
论文摘要
在这项工作中，我们介绍了一个称为HyperGraph神经网络（HNN）的HyperGraph表示学习框架，该框架共同学习HypereDge嵌入以及一组HyperGraph中每个节点的超边缘依赖性嵌入。 HNN衍生了每个节点的多个嵌入在超图中，其中每个节点的每个嵌入都取决于该节点的特定高度。值得注意的是，HNN具有准确，数据效率，具有许多可互换组件的灵活性，并且对于多种超图学习任务有用。我们评估了HNN框架对超边缘预测和超图节点分类的有效性。我们发现，HNN在所有基线模型和图表中分别实现了超边缘预测和超图节点分类的总体平均增益为7.72％和11.37％。
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.
