Are We There Yet? A Roadmap of Network Visualization from Surveys to Task Taxonomies

Abstract

Networks are abstract and ubiquitous data structures, defined as a set of data points and relationships between them. Network visualization provides meaningful representations of these data, supporting researchers in understanding the connections, gathering insights, and detecting and identifying unexpected patterns. Research in this field is focusing on increasingly challenging problems, such as visualizing dynamic, complex, multivariate, and geospatial networked data. This ever‐growing, and widely varied, body of research led to several surveys being published, each covering one or more disciplines of network visualization. Despite this effort, the variety and complexity of this research represents an obstacle when surveying the domain and building a comprehensive overview of the literature. Furthermore, there exists a lack of clarification and uniformity between the terminology used in each of the surveys, which requires further effort when mapping and categorizing the plethora of different visualization techniques and approaches. In this paper, we aim at providing researchers and practitioners alike with a “roadmap” detailing the current research trends in the field of network visualization. We design our contribution as a meta‐survey where we discuss, summarize, and categorize recent surveys and task taxonomies published in the context of network visualization. We identify more and less saturated disciplines of research and consolidate the terminology used in the surveyed literature. We also survey the available task taxonomies, providing a comprehensive analysis of their varying support to each network visualization discipline and by establishing and discussing a classification for the individual tasks. With this combined analysis of surveys and task taxonomies, we provide an overarching structure of the field, from which we extrapolate the current state of research and promising directions for future work.

Tiramisù: Making Sense of Multi-Faceted Process Information Through Time and Space

“…In the visualization literature, taxonomies have been introduced to formalize and categorize tasks. The excellent survey by Filipov et al [28] provides an extensive discussion on the existing task taxonomies in the literature. Last but not least, we devised Tiramisù to support a wider variety of potential users.…”

ProtEGOnist: Visual Analysis of Interactions in Small World Networks Using Ego‐graphs

“…Therefore, approaches beyond force-directed node-link diagrams, different layouts [SKL*14] including hierarchical layouts [AMA07], on-node encodings [VV14], and hybrid network visualizations [HFM07; ADM*19] have been developed. As an indepth review of the vast literature on network visualization is beyond the scope of our paper, we refer to the recent state-of-the-art reports by Filipov et al [FAM23] and Nobre et al [NMSL19].…”

“…Typical visualizations for small-world networks include nodelink diagrams or matrix representations [FAM23]. Links in our case represent any type of interaction, e.g., interacting proteins or "interacting" researchers co-authoring a paper.…”

“…This concept allows for an exploration from an overview level down to analyzing subsets of ego-graphs, comparing up to three ego-graphs in detail, and inspecting single ego-graphs (Section 3.3). Using the taxonomy proposed by Filipov et al [FAM23], we would position ProtEGOnist as a group network visualization, since the ego-graphs represent groups of nodes based on neighborhood. Using the Vertex Group Structure Taxonomy by Vehlow et al [VBW17], we would describe ProtEGOnist as an overlapping hierarchical structure, which they found only in a single approach.…”

DynTrix: A Hybrid Representation for Dynamic Graphs

“…Visualization of dynamic networks is a mature and thriving research field. Two recent and comprehensive surveys on the topic [BBDW17,FAM23] discuss experimenting with hybrid visualizations as a promising yet under-investigated research direction. Existing approaches (such as NodeTrix [HFM07] and Chordlink [ADM * 22]) show interesting results: however, the design space of hybrid representations is still far from being explored to its full potential.…”