Hypergraph convolutional neural network-based clustering technique
This work addresses clustering in graph-structured data for researchers, but it appears incremental as it combines existing hypergraph auto-encoders with k-means clustering.
The paper tackled the clustering problem for the Citeseer and Cora datasets by developing a hypergraph convolutional neural network-based clustering technique, which achieved better performance results compared to other classical clustering methods.
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.