Deep k-Means Node Clustering Based on Graph Neural Networks

Hyesoo Shin, Ki Yong Lee

http://doi.org/10.5626/JOK.2023.50.12.1153

Recently, graph node clustering techniques using graph neural networks (GNNs) have been actively studied. Notably, most of these studies use a GNN to embed each node into a low-dimensional vector and then cluster the embedding vectors using the existing clustering algorithms. However, since this approach does not consider the final goal of clustering when training the GNN, it is difficult to say that it produces optimal clustering results. Therefore, in this paper, we propose a deep k-means clustering method that iteratively trains a GNN considering the final goal of k-means clustering and performs k-means clustering on the embedding vectors generated by the trained GNN. The proposed method considers both the similarity between nodes and the loss of k-means clustering when training a GNN. Experimental results using real datasets confirmed that the proposed method improves the quality of k-means clustering results compared to the existing methods.