In November 2012, Waterloo Institute for Complexity & Innovation (WICI) sent out a call for submissions (PDF) seeking tools and methods designed to improve the exploration, analysis, and visualization of complex-systems data.
Attached to the challenge was a cash prize, valued at $10,000 (CAD), and an invitation to participate in WICI’s subsequent data visualization workshop.
We’re pleased to announce the winners of the WICI data challenge: Przemyslaw Grabowicz, Luca Aiello and Fil Menczer with their tool titled “Fast visualization of relevant portions of large dynamic networks.”
Below you’ll find information, documentation, video, and access to the source code for not only the winning tool, but also for the runners-up.
- "Fast visualization of relevant portions of large dynamic networks"
- "Early Warning Signals Toolbox:A Novel Approach for Detecting Critical Transitions"
- "JUST: A Network Simulation Toolkit for Complex Systems Researchers"
- "Visualizing Argentine Ants: The Use of Dance to Visualize Complex Data"
"Fast Visualization of Relevant Portions of Large Dynamic Networks" by Przemyslaw Grabowicz, Luca Aiello and Fil Menczer
Abstract:
Detecting and visualizing what are the most relevant changes in an evolving network is still an open challenge in several domains. We develop a fast algorithm that selects subsets of nodes and edges that best represent an evolving graph and visualize it by either creating a movie, or by streaming it to an interactive network visualization tool. Our code, that is already deployed in the movie generation tool of the OSoMe system, is limited in memory and processor time usage.
Access the "Fast Visualization of Relevant Portions of Large Dynamic Networks" project documentation and code on GitHub.
"Early Warning Signals Toolbox: A Novel Approach for Detecting Critical Transitions" by Vasilis Dakos and Leo Lahti
Abstract:
Here, we present our newly developed Early Warning Signal Toolbox designed for estimating and visualizing fingerprints of upcoming critical transitions based on time series data. The toolbox is characterized by three unique features: First, it is of a truly generic nature and can be applied to any system that may undergo critical transitions. Second, it is based on state-of-the-art methodology with already tested real-world examples in high-profile publications [10–15]. Third, it is easy to use through a user-friendly interface developed in R, an increasingly popular open-access statistical language for scientific computing.
"JUST: A Network Simulation Toolkit for Complex Systems Researchers" by Jon MacKay
Abstract:
In this paper I describe JUST — a software framework designed to help researchers rapidly develop custom simulation models of networks. JUST stands for JUNG Universal Simulation Toolkit. JUNG is an open-source project written in Java that focuses on network analysis (O’Madadhain et al. 2010). JUST is not just a framework to develop and run simulations, but a complete package that gives users the ability to visualize their models, share them with others and export the data they generate to other packages for analysis.
Code and associated documentation coming soon!
"Visualizing Argentine Ants: The Use of Dance to Visualize Complex Data" by Sarah Hogland and Elliott Miller
Abstract:
Complex systems data, regardless of their dimensions, are usually communicated on two-dimensional surfaces, such as in text, statistics, equations, graphs, flowcharts, and feedback diagrams. These classical means of data communication, though explicit and unambiguous, can often be difficult to interpret because of dense formal language conventions of science and math. Dance, as a means to personify complex data, facilitates audience investment in the issues presented, as well as room for creative interpretation of them.
Visualizing Argentine Ants: The Use of Dance to Visualize Complex Data from Waterloo Complexity Institute on Vimeo.