Lightweight Support Vector Clustering Algorithm for Community Detection in Complex Networks

The community structure is one of the most attractive properties of a complex network. This structure has been fundamental to advancements in various scientific branches. Numerous tools that involve community detection algorithms have been used in recent studies. In this paper, we propose a lightweight support vector clustering method. It surpasses traditional support vector approaches in terms of accuracy and complexity on account of its innovative design of distance calculations and the utilization of stable equilibrium points in the community assignment process. Extensive experiments are undertaken in computer-generated networks as well as real-world datasets. The results illustrate the competitive performance of the proposed algorithm compared to its community detection counterparts.