G2-SCANN: Gaussian-kernel graph-based SLD clustering algorithm with natural neighbourhood

Zhidong Deng, Jingyi Wang

Abstract: Highlights•The shortest path length (SPL) in complex network or graph-based geodesic distance is used to give a locally backbone-structured description of graph vertex similarity. Accordingly, SPL-weighted local degree (SLD) is defined as vertex attributes of a SPL-weighted graph expressed by G2-SPL adjacency matrix with natural neighbourhood.•The process of calculating SLD for every data point in a bottom-up way directly leads to di-vision from a complete graph constituted by all data points to a group of SLD trees. This brings the interpretability and the elimination of lone trees.•Contrastive learning of largest SLD values for finding root vertices of each divisive tree is conducted and top-down category message is then transmitted from the root vertices to all the leaf ones of a SLD tree. It eventually produces tree-like clusters.