One-Hot Graph Encoder Embedding

TL;DR

This paper introduces a highly efficient graph embedding method called one-hot graph encoder embedding, capable of processing billions of edges quickly on standard hardware, suitable for large-scale graph analysis.

Contribution

The paper presents a novel graph embedding technique with linear complexity, enabling fast processing of massive graphs and demonstrating its effectiveness in various applications.

Findings

Supports billions of edges within minutes

Approximately normally distributed embeddings under random graph models

Outperforms existing methods in vertex classification and clustering

Abstract

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.