Graph Max Shift: A Hill-Climbing Method for Graph Clustering

Autor:	Arias-Castro, Ery, Coda, Elizabeth, Qiao, Wanli
Rok vydání:	2024
Předmět:	Statistics - Machine Learning Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	We present a method for graph clustering that is analogous with gradient ascent methods previously proposed for clustering points in space. We show that, when applied to a random geometric graph with data iid from some density with Morse regularity, the method is asymptotically consistent. Here, consistency is understood with respect to a density-level clustering defined by the partition of the support of the density induced by the basins of attraction of the density modes.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2411.18794 Zobrazit plný text záznamu View this record from Arxiv