Exploring the Limits of Complexity: A Survey of Empirical Studies on Graph Visualisation

TL;DR

This survey reviews empirical research on how various features of node-link diagrams influence the perceived visual complexity of large and complex networks, considering human factors and technological aspects.

Contribution

It provides a comprehensive overview of empirical studies examining the impact of diagram features on visual complexity in graph visualization.

Findings

Different features significantly affect perceived complexity

Technological context influences complexity perception

Data and visual complexity interplay affects readability

Abstract

For decades, researchers in information visualisation and graph drawing have focused on developing techniques for the layout and display of very large and complex networks. Experiments involving human participants have also explored the readability of different styles of layout and representations for such networks. In both bodies of literature, networks are frequently referred to as being 'large' or 'complex', yet these terms are relative. From a human-centred, experiment point-of-view, what constitutes 'large' (for example) depends on several factors, such as data complexity, visual complexity, and the technology used. In this paper, we survey the literature on human-centred experiments to understand how, in practice, different features and characteristics of node-link diagrams affect visual complexity.