DRGraph: An Efficient Graph Layout Algorithm for Large-scale Graphs by Dimensionality Reduction

TL;DR

DRGraph is a scalable graph layout algorithm that combines approximation, negative sampling, and multi-level optimization to efficiently produce high-quality layouts for large graphs with millions of nodes.

Contribution

It introduces a novel combination of techniques to reduce complexity and improve scalability in large-scale graph layout computation.

Findings

Achieves linear complexity in computation and memory.

Generates visually comparable layouts faster than existing methods.

Scales efficiently to graphs with millions of nodes.

Abstract

Efficient layout of large-scale graphs remains a challenging problem: the force-directed and dimensionality reduction-based methods suffer from high overhead for graph distance and gradient computation. In this paper, we present a new graph layout algorithm, called DRGraph, that enhances the nonlinear dimensionality reduction process with three schemes: approximating graph distances by means of a sparse distance matrix, estimating the gradient by using the negative sampling technique, and accelerating the optimization process through a multi-level layout scheme. DRGraph achieves a linear complexity for the computation and memory consumption, and scales up to large-scale graphs with millions of nodes. Experimental results and comparisons with state-of-the-art graph layout methods demonstrate that DRGraph can generate visually comparable layouts with a faster running time and a lower memory requirement.