Dynamical.JS: A composable framework for online exploratory visualization of arbitrarily-complex multivariate networks

Abstract

Multivariate networks (henceforth, graphs) represent entities (vertices or nodes), their relationships to each other (edges), and manifest or derived data about both (attributes). Graphs easily map onto real-world entities and relationships. Therefore the visual analysis of such provide a valuable proxy for the analysis of concrete entities. Because graphs are so effective in modeling, they are ubiquitous: graphs are used in the design and construction of complex circuitry, GIS (Geographic Information Systems), database modeling, social networking, and neuroscience, among other fields. Graph visualization is challenging, and domain-specific requirements further complicate the production of intelligible visual representations. The datasets underlying modern graphs have grown exponentially, complicating visualization tasks, whether static, such as in a printed scientific paper or poster, or dynamic, where the graph, the visualization, or both evolve. Exploratory visualization of dynamical graphs requires the visualizations to evolve due to externally-generated events in realtime while preserving the contents of the analyst’s “mental map.” This thesis presents a novel, modular, composable Javascript API (Application Programming Interface) for organizing and applying the algorithmic operations and data structures required at each step of visualizing large, arbitrarily complex graphs while allowing the extensions and constraints required by specific application domains. To do so, we abstract quintessential graph layout tasks into EGOs (Exploratory Graph Operations), design an API exposing these operations, and demonstrate the synthesis of well-documented layout algorithms. Finally, we introduce a reference implementation in Javascript and WebGPU (GPU Computing for the Web) to visualize complex graphs in realtime and facilitate future research and development of novel approaches to these problems.