Hypergraph convolutional neural network-based clustering technique

TL;DR

This paper introduces a novel hypergraph convolutional neural network-based clustering method that leverages hypergraph auto-encoders and k-means to improve clustering performance on datasets like Citeseer and Cora.

Contribution

The paper presents a new hypergraph CNN-based clustering approach that combines auto-encoders and k-means, outperforming classical clustering methods.

Findings

Better clustering performance than classical methods

Effective use of hypergraph auto-encoders for dimensionality reduction

Successful application to Citeseer and Cora datasets

Abstract

This paper constitutes the novel hypergraph convolutional neural networkbased clustering technique. This technique is employed to solve the clustering problem for the Citeseer dataset and the Cora dataset. Each dataset contains the feature matrix and the incidence matrix of the hypergraph (i.e., constructed from the feature matrix). This novel clustering method utilizes both matrices. Initially, the hypergraph auto-encoders are employed to transform both the incidence matrix and the feature matrix from high dimensional space to low dimensional space. In the end, we apply the k-means clustering technique to the transformed matrix. The hypergraph convolutional neural network (CNN)-based clustering technique presented a better result on performance during experiments than those of the other classical clustering techniques.