Graph Encoder Ensemble for Simultaneous Vertex Embedding and Community Detection

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.

arXiv comment9 pages

References in corpus (10)

• Fast unfolding of communities in large networks
• Community structure in social and biological networks
• Community detection in graphs
• From Louvain to Leiden: guaranteeing well-connected communities
• Natural Scales in Geographical Patterns
• Popular Ensemble Methods: An Empirical Study
• Stochastic blockmodels and community structure in networks
• Spectral clustering and the high-dimensional stochastic blockmodel
• Consistency of community detection in networks under degree-corrected stochastic block models
• One-Hot Graph Encoder Embedding

Cited by in corpus (5)

• Discovering Communication Pattern Shifts in Large-Scale Labeled Networks using Encoder Embedding and Vertex Dynamics
• Encoder Embedding for General Graph and Node Classification
• Fast and Scalable Multi-Kernel Encoder Classifier
• Refined Graph Encoder Embedding via Self-Training and Latent Community Recovery
• Efficient Graph Encoder Embedding for Large Sparse Graphs in Python