Economical representation of spatial networks

TL;DR

This paper introduces a novel graph filtering optimization approach for visualizing spatial networks, enhancing readability by reducing edge crossings without node rearrangement, inspired by ecological principles.

Contribution

It presents a new method that transforms the edge crossing problem into an optimization task, improving spatial network visualization when node movement isn't possible.

Findings

Longer connections lead to sparser, more readable networks.

The approach aligns with human perception of network layouts.

Provides an ecologically-inspired criterion for network visualization.

Abstract

Network visualization is essential for many scientific, societal, technological and artistic domains. The primary goal is to highlight patterns out of nodes interconnected by edges that are easy to understand, facilitate communication and support decision-making. This is typically achieved by rearranging the nodes to minimize the edge crossings responsible of unintelligible and often unaesthetic trends. But when the nodes cannot be moved, as in spatial and physical networks, this procedure is not viable. Here, we overcome this situation by turning the edge crossing problem into a graph filtering optimization. We demonstrate that the presence of longer connections prompt the optimal solution to yield sparser networks, thereby limiting the number of intersections and getting more readable layouts. This theoretical result matches human behavior and provides an ecologically-inspired criterion to visualize and model real-world interconnected systems.