Graph Encoder Ensemble for Simultaneous Vertex Embedding and Community Detection

TL;DR

This paper presents a new graph encoder ensemble method that efficiently performs vertex embedding, community detection, and community size estimation with strong numerical results.

Contribution

It introduces a normalized one-hot graph encoder combined with a rank-based community size measure, offering a novel approach for simultaneous embedding and community detection.

Findings

Excellent numerical performance demonstrated in simulations

Efficient and computationally effective method

Simultaneous vertex embedding and community detection achieved

Abstract

In this paper, we introduce a novel and computationally efficient method for vertex embedding, community detection, and community size determination. Our approach leverages a normalized one-hot graph encoder and a rank-based cluster size measure. Through extensive simulations, we demonstrate the excellent numerical performance of our proposed graph encoder ensemble algorithm.