Line Hypergraph Convolution Network: Applying Graph Convolution for Hypergraphs
Line Hypergraph Convolution Network: Applying Graph Convolution for Hypergraphs
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发表时间：
2020-02
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通讯作者：
S. Bandyopadhyay;Kishalay Das;M. Murty
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作者：
S. Bandyopadhyay;Kishalay Das;M. Murty
随着各种图神经网络的出现，图中的网络表示学习和节点分类得到了广泛的关注。图卷积网络（GCN）是一种流行的半监督技术，它聚集每个节点邻域内的属性。传统的GCN可以应用于每个边仅连接两个节点的简单图。但是，许多现代应用程序需要在图中建模高阶关系。超图是处理这种复杂关系的有效数据类型。在本文中，我们提出了一种新的技术，应用图卷积的超图具有可变的超边大小。在超图学习文献中，我们首次使用超图的线图的经典概念。然后，我们提出使用图卷积的线图超图。在多个真实的世界网络数据集上的实验分析表明，我们的方法相比，国家的艺术的优点。
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.