In many practical datasets, such as co-citation and co-authorship, relationships across the samples are more complex than pair-wise. Hypergraphs provide a flexible and natural representation for such complex correlations and thus obtain increasing attention in the machine learning and data mining communities. Existing deep learning-based hypergraph approaches seek to learn the latent vertex representations based on either vertices or hyperedges from previous layers and focus on reducing the cross-entropy error over labeled vertices to obtain a classifier. In this paper, we propose a novel model called Hypergraph Collaborative Network (HCoN), which takes the information from both previous vertices and hyperedges into consideration to achieve informative latent representations and further introduces the hypergraph reconstruction error as a regularizer to learn an effective classifier. We evaluate the proposed method on two cases, i.e., semi-supervised vertex and hyperedge classifications. We carry out the experiments on several benchmark datasets and compare our method with several state-of-the-art approaches. Experimental results demonstrate that the performance of the proposed method is better than that of the baseline methods.

中文翻译：

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顶点和超边上的超图协作网络


在许多实际数据集中，例如同被引和共同作者，样本之间的关系比成对的关系更复杂。超图为这种复杂的相关性提供了灵活、自然的表示，因此在机器学习和数据挖掘社区中获得了越来越多的关注。现有的基于深度学习的超图方法寻求基于先前层的顶点或超边来学习潜在顶点表示，并专注于减少标记顶点的交叉熵误差以获得分类器。在本文中，我们提出了一种称为超图协作网络（HCoN）的新模型，该模型考虑来自先前顶点和超边的信息以实现信息丰富的潜在表示，并进一步引入超图重建误差作为正则化器来学习有效的分类器。我们在两种情况下评估所提出的方法，即半监督顶点和超边分类。我们在几个基准数据集上进行了实验，并将我们的方法与几种最先进的方法进行了比较。实验结果表明，该方法的性能优于基线方法。