论文标题
线超图卷积网络：应用图形卷积
Line Hypergraph Convolution Network: Applying Graph Convolution for Hypergraphs
论文作者
论文摘要
由于不同类型的图形神经网络的发明，网络表示学习和图表中的节点分类受到了极大的关注。图形卷积网络（GCN）是一种流行的半监督技术，它汇总了每个节点附近的属性。传统的GCN可以应用于每个边缘仅连接两个节点的简单图。但是，许多现代应用需要在图中建模高级关系。超图是处理这种复杂关系的有效数据类型。在本文中，我们提出了一种新颖的技术，以在具有可变的超边缘大小的超图上应用图形卷积。我们在HyperGraph学习文献中首次使用HyperGraph的线图的经典概念。然后，我们建议在超图的线图上使用图形卷积。对多个现实世界网络数据集的实验分析显示了与最先进的方法相比，我们的方法的优点。
Network representation learning and node classification in graphs got significant attention due to the invent of different types graph neural networks. Graph convolution network (GCN) is a popular semi-supervised technique which aggregates attributes within the neighborhood of each node. Conventional GCNs can be applied to simple graphs where each edge connects only two nodes. But many modern days applications need to model high order relationships in a graph. Hypergraphs are effective data types to handle such complex relationships. In this paper, we propose a novel technique to apply graph convolution on hypergraphs with variable hyperedge sizes. We use the classical concept of line graph of a hypergraph for the first time in the hypergraph learning literature. Then we propose to use graph convolution on the line graph of a hypergraph. Experimental analysis on multiple real world network datasets shows the merit of our approach compared to state-of-the-arts.
