Advancing Local Clustering on Graphs via Compressive Sensing: Semi-supervised and Unsupervised Methods

ArXi:2504.19419v3 Announce Type: replace Local clustering aims to identify specific substructures within a large graph without any additional structural information of the graph. These substructures are typically small compared to the overall graph, enabling the problem to be approached by finding a sparse solution to a linear system associated with the graph Laplacian. In this work, we first propose a method for identifying specific local clusters when very few labeled data are given, which we term semi-supervised local clustering.