Collaborative Research: Framework: Software: HDR: Reproducible Visual Analysis of Multivariate Networks with MultiNet

Abstract

Multivariate networks – datasets that link together entities that are associated with multiple different variables – are a critical data representation for a range of high-impact problems, from understanding how our bodies work to uncovering how social media influences society. These data representations are a rich and complex reflection of the multifaceted relationships that exist in the world. Reasoning about a problem using a multivariate network allows an analyst to ask questions beyond those about explicit connectivity alone: Do groups of social-media influencers have similar backgrounds or experiences? Do species that co-evolve live in similar climates? What patterns of cell-types support different types of brain functions? Questions like these require understanding patterns and trends about entities with respect to both their attributes and their connectivity, leading to inferences about relationships beyond the initial network structure. As data continues to become an increasingly important driver of scientific discovery, datasets of networks have also become increasingly complex. These networks capture information about relationships between entities as well as attributes of the entities and the connections. Tools used in practice today provide very limited support for reasoning about networks and are also limited in the how users can interact with them. This lack of support leaves analysts and scientists to piece together workflows using separate tools, and significant amounts of programming, especially in the data preparation step. This project aims fill this critical gap in the existing cyber-infrastructure ecosystem for reasoning about multivariate networks by developing MultiNet, a robust, flexible, secure, and sustainable open-source visual analysis system.

MultiNet aims to change the landscape of visual analysis capabilities for reasoning about and analyzing multivariate networks. The web-based tool, along with an underlying plug-in-based framework, will support three core capabilities:

  1. interactive, task-driven visualization of both the connectivity and attributes of networks,
  2. reshaping the underlying network structure to bring the network into a shape that is well suited to address analysis questions, and
  3. leveraging provenance data to support reproducibility, communication, and integration in computational workflows.

These capabilities will allow scientists to ask new classes of questions about network datasets, and lead to insights about a wide range of pressing topics. To meet this goal, we will ground the design of MultiNet in four deeply collaborative case studies with domain scientists in biology, neuroscience, sociology, and geology.

Software

Multinet is web-based, open-source software. You can find the interactive application at:

The source code for the projects is available at:

Origraph, our network wrangling prototype is available at:

Our ranking method for multivariate network visualization techniques is available at:

Publications

revisit screenshot

Carolina Nobre, Dylan Wootton, Zach Cutler, Lane Harrison, Hanspeter Pfister, Alexander Lex
reVISit: Looking Under the Hood of Interactive Visualization Studies
SIGCHI Conference on Human Factors in Computing Systems (CHI) (to appear), 2021

insights screenshot

Jen Rogers, Austin H Patton, Luke Harmon, Alexander Lex, Miriah Meyer
Insights From Experiments With Rigor in an EvoBio Design Study
IEEE Transactions on Visualization and Computer Graphics (InfoVis), 2021

clipped graphs poster screenshot

Haihan Lin, Carolina Nobre, Amanda Bakian, Alexander Lex
Clipped Graphs: A Compact Time-Series Encoding
Proceedings of the IEEE Information Visualization Conference – Posters (InfoVis), 2019

Origraph screenshot

Alex Bigelow, Carolina Nobre, Miriah Meyer, Alexander Lex
Origraph: Interactive Network Wrangling
IEEE Conference on Visual Analytics Science and Technology (VAST), 2019

VDL Project Staff

 Miriah Meyer  Alexander Lex  Jack Wilburn  Derya Akbaba

VDL Project Alumni

  • Mark Dewy
  • Jen Rogers

Other Staff

  • Roni Choudhury (Kitware)
  • Jake Conway (Kitware)

Funded by

The National Science Foundation

Number

 NSF OAC 1835904
 Collaborative Award

Program

DATANET, Software Institutes

Period

01/01/2019-12/31/2022

Principal Investigator

Co-Principal Investigator(s):

Co-Investigator(s):

  • Bryan Jones
  • Christopher Bail
  • Shaunna Morrison

Institutions:

  • University of Utah
  • Kitware
  • University of Idaho
  • Knowledge Vis
  • Duke University
  • Carnegie Institution of Science

Awarded Amount:

$ 2,022,200

Share at Utah:

$ 1,115,768

Last Updated

2021-01-13