Graph Encoder Ensemble for Simultaneous Vertex Embedding and Community Detection
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.
Code (1)
Tasks
Community DetectionSimilar Papers 제목 키워드 기반
One-Hot Graph Encoder Embedding
In this paper we propose a lightning fast graph embedding method called one-hot graph encoder embedding. It has a linear computational complexity and the capacity to process billions of edges within minutes on standard P…
ClusteringGraph EmbeddingStochastic Block ModelPrincipal Graph Encoder Embedding and Principal Community Detection
In this paper, we introduce the concept of principal communities and propose a principal graph encoder embedding method that concurrently detects these communities and achieves vertex embedding. Given a graph adjacency m…
Community DetectionRefined Graph Encoder Embedding via Self-Training and Latent Community Recovery
This paper introduces a refined graph encoder embedding method, enhancing the original graph encoder embedding through linear transformation, self-training, and hidden community recovery within observed communities. We p…
Synergistic Graph Fusion via Encoder Embedding
In this paper, we introduce a method called graph fusion embedding, designed for multi-graph embedding with shared vertex sets. Under the framework of supervised learning, our method exhibits a remarkable and highly desi…
ClassificationGraph EmbeddingStochastic Block ModelSimultaneous Dimensionality and Complexity Model Selection for Spectral Graph Clustering
Our problem of interest is to cluster vertices of a graph by identifying underlying community structure. Among various vertex clustering approaches, spectral clustering is one of the most popular methods because it is ea…
ClusteringGraph ClusteringModel SelectionSpectral Graph Clustering+1