Anisotropic Radial Layout for Visualizing Centrality and Structure in Graphs

TL;DR

This paper introduces a flexible anisotropic radial layout method for undirected graphs that effectively visualizes both centrality and structure by constraining nodes to nested curves, improving upon previous radial layouts.

Contribution

It proposes a novel layout technique that generalizes circular constraints to nested curves, integrating centrality estimation with multidimensional scaling for enhanced graph visualization.

Findings

Effective visualization of social networks demonstrated

Layout preserves vertex relationships better than existing methods

Method successfully captures structural centrality and vertex distances

Abstract

This paper presents a novel method for layout of undirected graphs, where nodes (vertices) are constrained to lie on a set of nested, simple, closed curves. Such a layout is useful to simultaneously display the structural centrality and vertex distance information for graphs in many domains, including social networks. Closed curves are a more general constraint than the previously proposed circles, and afford our method more flexibility to preserve vertex relationships compared to existing radial layout methods. The proposed approach modifies the multidimensional scaling (MDS) stress to include the estimation of a vertex depth or centrality field as well as a term that penalizes discord between structural centrality of vertices and their alignment with this carefully estimated field. We also propose a visualization strategy for the proposed layout and demonstrate its effectiveness using three social network datasets.