Abstract
Advances in data generation and acquisition have caused a volume of available data of such magnitude that our ability to interpret and extract valuable knowledge from them has been surpassed. Our capacity to analyze data is hampered not only by their amount or their dimensionality but also by their relationships and by the complexity of the systems they model. Compound graphs allow us to represent the existing relationships between nodes that are themselves hierarchically structured, so they are a natural substrate to support multiscale analysis of complex graphs. This paper presents Carbonic, a framework for interactive multiscale visual exploration and editing of compound graphs that incorporates several strategies for complexity management. It combines the representation of graphs at multiple levels of abstraction, with techniques for reducing the number of visible elements and for reducing visual cluttering. This results in a tool that allows both the exploration of existing graphs and the visual creation of compound graphs following a top-down approach that allows to simultaneously observe the entities and their relationships at different scales. The results show the applicability of the developed framework to two use cases, demonstrating the usefulness of Carbonic for moving from information to knowledge.
Screenshots
Videos
User Manual
The manual for the latest version is available in pdf format.
Examples
Carbonic: visualizing and creating complex compound graphs
Release:
- Airports example: This example represents all the airports in the world and their direct routes. The hierarchy is made up of levels: continents, country, region, municipality and aiports. You can find the domain formalization here.
- Course example: This example represents all the concepts in a subject and their relationships. The hierarchy is made up of levels: modules, units, lessons and concepts. You can find the domain formalization here.
- Empty canvas: Basic version of the framework without formalization of the domain. Lets you generate a compound graph without direction of any number of levels in the hierarchy.
Acknowledgments
The research leading to these results has received funding from the Spanish Ministry of Economy and Competitiveness under grants C080020-09 (Cajal Blue Brain Project, Spanish partner of the Blue Brain Project initiative from EPFL) and TIN2017-83132, and the Spanish Ministry of Science and Innovation under grants PID2020-113013RB-C21 and PID2020-113013RB-C22, as well as from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreements No. 785907 (Human Brain Project SGA2) and 945539 (Human Brain Project SGA3).