Collaborative contrastive learning for hypergraph node classification

Plenty of models have been presented to handle the hypergraph node classification. However, very few of these methods consider contrastive learning, which is popular due to its great power to represent instances. This paper makes an attempt to leverage contrastive learning to hypergraph representation learning. Specifically, we propose a novel method called Collaborative Contrastive Learning (CCL), which incorporates a generated standard graph with the hypergraph. The main technical contribution here is that we develop a collaborative contrastive schema, which performs contrast between the node views obtained from the standard graph and hypergraph in each network layer, thus making the contrast collaborative. To be precise, in the first layer, the view from the standard graph is used to augment that from the hypergraph. Then, in the next layer, the augmented features are adopted to train a new representation to augment the view from the standard graph conversely. With this setting, the learning procedure is alternated between the standard graph and hypergraph. As a result, the learning on the standard graph and hypergraph is collaborative and leads to the final informative node representation. Experimental results on several widely used datasets validate the effectiveness of the proposed model.