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 PC -- making it an ideal candidate for huge graph processing. It is applicable to either adjacency matrix or graph Laplacian, and can be viewed as a transformation of the spectral embedding. Under random graph models, the graph encoder embedding is approximately normally distributed per vertex, and asymptotically converges to its mean. We showcase three applications: vertex classification, vertex clustering, and graph bootstrap. In every case, the graph encoder embedding exhibits unrivalled computational advantages.
Code (3)
Tasks
ClusteringGraph EmbeddingStochastic Block ModelMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
ReGAE: Graph autoencoder based on recursive neural networks
Invertible transformation of large graphs into fixed dimensional vectors (embeddings) remains a challenge. Its overcoming would reduce any operation on graphs to an operation in a vector space. However, most existing met…
DecoderAdversarially Regularized Graph Autoencoder for Graph Embedding
Graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics. Most existing embedding algorithms typically focus on preserving the topological structure or minimizing the …
ClusteringDecoderGraph ClusteringGraph Embedding+1Principal 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 DetectionVariational Graph Normalized Auto-Encoders
Link prediction is one of the key problems for graph-structured data. With the advancement of graph neural networks, graph autoencoders (GAEs) and variational graph autoencoders (VGAEs) have been proposed to learn graph …
Link PredictionPredictionGraph 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-b…
Community Detection