A Study on Network Clustering with Node Embedding

Abstract

Network data consists of vertex nodes and edges connecting nodes. Network data can represent various real-world events as networks, such as SNS follow-up relationships and railway lines. Analysing network data is important in that it provides insights that take into account not only information about nodes, but also the relationships between them. However, existing network analysis methods have issues such as high computational cost and a limited number of analysis methods. Node embedding is a method that embeds network data into a low-dimensional vector space. This allows network data to be treated as vector data and enables visualization and classification using machine learning methods. In this study, we focus on network clustering using node embedding. Experimental results show that network clustering with node embedding outperforms existing methods in some cases. This suggests the usefulness of clustering using node embedding as a new clustering method for network data.