GMap: Drawing Graphs as Maps

TL;DR

GMap is a visualization tool that creates geographic-like maps to better represent the structure, clustering, and neighborhoods in relational data, improving interpretability over traditional dimensionality reduction methods.

Contribution

This paper introduces GMap, a novel visualization approach that maps relational data into geographic-like maps, capturing structural information more effectively.

Findings

GMap effectively visualizes complex relational data.

Maps reveal underlying data structures and clusters.

Demonstrated across multiple domain examples.

Abstract

Information visualization is essential in making sense out of large data sets. Often, high-dimensional data are visualized as a collection of points in 2-dimensional space through dimensionality reduction techniques. However, these traditional methods often do not capture well the underlying structural information, clustering, and neighborhoods. In this paper, we describe GMap: a practical tool for visualizing relational data with geographic-like maps. We illustrate the effectiveness of this approach with examples from several domains All the maps referenced in this paper can be found in http://www.research.att.com/~yifanhu/GMap