PDF III: Medium: Collaborative Research:
Topological Data Analysis for Large Network Visualization

Award Number and Duration

NSF IIS 1513616

NSF IIS 1513651

September 1, 2015 to August 31, 2019 (Estimated)

Point of Contact

Bei Wang
Assistant Professor
School of Computing and Scientific Computing and Imaging Institute
University of Utah
beiwang AT sci.utah.edu
http://www.sci.utah.edu/~beiwang

PI and Co-PIs

Bei Wang (PI)
Assistant Professor
School of Computing and Scientific Computing and Imaging Institute
University of Utah
beiwang AT sci.utah.edu
http://www.sci.utah.edu/~beiwang

Carlos Scheidegger (Co-PI)
Assistant Professor
Department of Computer Science
University of Arizona
cscheid AT email.arizona.edu
https://cscheid.net/

Paul Rosen (Co-PI)
Assistant Professor
Computer Science and Engineering
University of South Florida
prosen AT usf.edu
http://www.cspaul.com/wordpress/

Overview

This project leverages topological methods to develop a new class of data analysis and visualization techniques to understand the structure of networks. Networks are often used in modeling social, biological and technological systems, and capturing relationships among individuals, businesses, and genomic entities. Understanding such large, complex data sources is highly relevant and important in application areas including brain connectomics, epidemiology, law enforcement, public policy and marketing. The proposed research will be evaluated over multiple data sources, including but not limited to large social, communication and brain network datasets. Furthermore, the new approaches developed in this project will be integrated into growing data analysis curricula, shared through developing workshops, and used as topics to continue attracting underrepresented groups into STEM fields and computer science specifically.

The scientific challenges this project addresses are two-fold: how to use topology to extract features from the data; and how to design effective visualizations to communicate these features to domain experts and decision makers. Topological techniques central to this project provide a strong theoretical basis for simplifying and summarizing complex data while still preserving critical underlying structures. They also provide a basis for task-oriented designs that allow us to control the volume of data to be displayed in visualizations, so users can develop faithful mental models of the data, facilitating information discovery. This project focuses on two research agendas. First, it proposes a rich body of topological summarization techniques to extract and preserve important topological features within large-scale graph-structured networks, and to obtain compact and hierarchical representations that are suitable for visual exploration. The feature extracting process captures complex interactions in the system, describes features at all scales, is robust with respect to noise, and has efficient computation. Second, this project proposes designing visualizations that encode the extracted topological structures explicitly, focusing on investigating techniques to fully exploit their properties in the visual metaphors to be developed. This project web site provides additional information and will include access to developed tools and test data sets.

Journal Publications / Book Chapters

Papers marked with * use alphabetic ordering of authors.
Students are underlined.
2017
PDF Gaussian Cubes: Real-Time Modeling for Visual Exploration of Large Multidimensional Datasets.
Zhe Wang, Nivan Ferreira, Youhao Wei, Aarthy Sankari Bhaskar and Carlos Scheidegger.
IEEE Transactions on Visualization and Computer Graphics (TVCG), 2017.
2016
PDF Critical Point Cancellation in 3D Vector Fields: Robustness and Discussion.
Primoz Skraba, Paul Rosen, Bei Wang, Guoning Chen, Harsh Bhatia and Valerio Pascucci.
IEEE Transactions on Visualization and Computer Graphics (TVCG), 22(6), pages 1683-1693, 2016.
Supplemental Video. Vortex Video.

Conference Publications

2016
PDF Gaussian Cubes: Real-Time Modeling for Visual Exploration of Large Multidimensional Datasets.
Zhe Wang, Nivan Ferreira, Youhao Wei, Aarthy Sankari Bhaskar and Carlos Scheidegger.
Proceedings IEEE Visualization Conference (VIS), 2016.
PDF Kernel Partial Least Squares Regression for Relating Functional Brain Network Topology to Clinical Measures of Behavior.
Eleanor Wong, Sourabh Palande, Bei Wang, Brandon Zielinski, Jeffrey Anderson and P. Thomas Fletcher.
International Symposium on Biomedical Imaging (ISBI), 2016.
Poster presentations at ISBI by Eleanor Wong and at TGDA@OSU by Sourabh Palande.
PDF Convergence between Categorical Representations of Reeb Space and Mapper.
Elizabeth Munch and Bei Wang*.
International Symposium on Computational Geometry (SOCG), 2016.
arXiv Version: arXiv:1512.04108. Invited Talk at TGDA@OSU.
PDF Exploring Persistent Local Homology in Topological Data Analysis.
Brittany T. Fasy and Bei Wang*.
Special session on Topological Methods in Data Science and Analysis,
IEEE International Conference on Acoustics, Speech and Signal Process (ICASSP), 2016.
PDF Critical Point Cancellation in 3D Vector Fields: Robustness and Discussion.
Primoz Skraba, Paul Rosen, Bei Wang, Guoning Chen, Harsh Bhatia and Valerio Pascucci.
Proceedings IEEE Pacific Visualization (PacificVis), 2016. Best Paper Award!
Supplemental Video. Vortex Video.

Presentations

Conference Talk by Zhe Wang: Gaussian Cubes: Real-Time Modeling for Visual Exploration of Large Multidimensional Datasets, at IEEE Visualization Conference, Baltimore, Maryland. October 27, 2016.

Invited Talk by Bei Wang: Topology, Geometry, and Data Analysis Conference at Ohio State University, Columbus, Ohio. May 16 to 20, 2016. Conference Webpage.

Poster presentation by Sourabh Palande: Topology, Geometry, and Data Analysis Conference at Ohio State University, Columbus, Ohio. May 16, 2016.

Distinguished Lecture by Bei Wang: Understanding the Shape of Data with Topological Data Analysis and Visualization, from Vector Fields to Brain Networks, Norrköping Visualization Center, Linköping University Norrköping Campus, Sweden, May 4th, 2016.

Conference Talk by Bei Wang: The 9th IEEE Pacific Visualization Symposium, Taipei, Taiwan. April 19 to 22, 2016. Conference Webpage.

Educational Development and Broader Impacts

Lecture by Bei Wang: at Hi-GEAR (Girls Engineering Abilities Realized) Camp, part of Engineering Summer Camps at the University of Utah, June 13-17, 2016. Hi-GEAR is designed to expose young women (currently in 9th-12th grade) to a variety of engineering and computer science careers with hands-on experiential learning and collaborative team projects.

Students and Postdocs

Sourabh Palande (Graduate Research Assistant)
School of Computing and Scientific Computing and Imaging Institute
University of Utah
sourabh AT sci.utah.edu
https://www.sci.utah.edu/people/sourabh.html

Todd Harry Reeb (Graduate Research Assistant, Summer 2016)
Department of Mathematics
University of Utah
reeb AT math.utah.edu
http://www.math.utah.edu/~reeb/

Mustafa Hajij (Postdoc)
Department of Mathematics & Statistics
University of South Florida
mhajij AT usf.edu
http://mhajij.myweb.usf.edu/

Junyi Tu (Graduate Research Assistant)
Department of Mathematics and Statistics
University of South Florida
junyi AT mail.usf.edu
http://math.usf.edu/people/gradta/jtu/

Jackson Pawson (Undergraduate Research Assistant)
Computer Science and Engineering
University of South Florida
jacksonpawson AT gmail.com

Collaborators

Tom Fletcher
Braxton Osting
Elizabeth Munch
Brandon Zielinski

Acknowledgement

This material is based upon work supported or partially supported by the National Science Foundation under Grant No.1513616 and 1513651, project titled "III: Medium: Collaborative Research: Topological Data Analysis for Large Network Visualization."

Any opinions, findings, and conclusions or recommendations expressed in this project are those of author(s) and do not necessarily reflect the views of the National Science Foundation.

Web page last update: May 29, 2016.