NSF IIS 1741536

BIGDATA: F: Critical Visualization Technologies for Analyzing & Understanding Big Network Data

Principal Investigator: Kwan-Liu Ma, University of California at Davis
Co-PI: Robert Faris, University of California at Davis

Program Manager: Maria Zemankova

Period: October 1, 2017 - Septermber 30, 2020 (No-cost extension to 9/30/2022)

Project Summary

Big data presents both opportunities and challenges to all fields of study and practice. Visualization has been proven effective as a knowledge discovery and storytelling tool for big data. This project aims to develop new visualization technologies for big network data that will both illustrate empirical findings and generate new discoveries. Although many network visualization techniques and tools have been introduced, visualizing large, dynamic networks to extract key entities, structures, and trends from the network data remains a challenging task. Most of the existing network visualization solutions were not designed for handling dynamic networks and are too slow for interactive exploration of large networks. This project will closely examine the integral parts of a holistic solution for the problem of big network visualization. The research study will be largely driven by the data analysis needs of sociological studies such as finding hidden associations between multiple networks; however, the project team will also investigate the solution's applicability in areas such as emergency management, life science and cyber security. The resulting technologies are expected to drastically enhance one's ability to explore and understand large, complex dynamic networks for knowledge discovery, critical decision making, and storytelling. This research effort is timely because of the explosive growth of data and common use of graphs as both the internal data structure and a visual representation in data-driven applications. Those who must deal with large, complex dynamic network data for their work will benefit from the advanced visualization technologies resulted from this research project. Students participating in this project will acquire strong interdisciplinary research skills for real-world problem solving.

This research underscores the importance of providing a comprehensive solution to the understanding of big data containing complex relations, structure, and trends. Primary research topics are: (1) Visual depiction and exploration of big network data; (2) Modeling and visualizing dynamic network data; (3) Visual monitoring and analysis of live, streaming network data; and (4) Provenance and storytelling with dynamic network data. This project will explore and integrate new network modeling, reduction, and visualization techniques for analyzing large, multivariate dynamic graphs. The resulting research innovations will both enhance existing methods and investigate new approaches to dynamic network visual analytics and drastically improve their usability for real-world applications. The targeted applications, emergency service and sociology, present the project team with some of the most challenging problems to address in making sense of heterogeneous dynamic big networks data. The collaborating domain experts are fully committed to participating in the evaluation work, which promises to produce usable technologies that will enable respondents to look at the data in new ways and uncover intricate relations among different entities/events for critical decision making and mitigation planning. The project results will be disseminated to the visualization community and beyond through annual conferences, workshops, and tutorials, and also through the project website which will include project status updates and resulting images, videos, and prototype software.

NSF Award Abstract Page


Participants

  • Kwan-Liu Ma, PI, UC Davis
  • Robert Faris, Co-PI, UC Davis
  • Takanori Fujiwara, PhD Student (graduated December 2021)
  • Oh-Hyun Kwon, PhD Student (graduated December 2020)
  • Tarik Crnovrsanin, PhD Student (graduated June 2019)
  • Annie Preston, PhD Student (graduated December 2019)
  • Keshav Dasu, PhD Student
  • Yun-Hsin Kuo, PhD Student
  • Yiran Li, PhD Student
  • Lukas Masopust, MS Student (graduate August 2022)
  • Maksim Gomov, MS Student (graduated June 2020)
  • Sandra Bae, MS Student (Former REU student; graduated June 2020)
  • Joseph Kotlarek, MS Student (graduated August 2019)
  • Shidi Yu, MS Student (graduated December 2021)
  • Hidekazu Shidara, Undergraduate Student (graduated August 2018)
  • Navya Gupta, Undergraduate Student (graduated)
  • Yuqi Yang, Undergarduate Student (REU; graduated)
  • Kirby Zhou, Undergraduate Student (REU; graduated)

Publications

  • Takanori Fujiwara, Shilpika, Naohisa Sakamoto, Jorji Nonaka, Keiji Yamamoto, Kwan-Liu Ma: A Visual Analytics Framework for Reviewing Multivariate Time-Series Data with Dimensionality Reduction. IEEETransactions on Visualization and Computer Graphics 27(2):1601-1611 (2021) [DOI]

  • Tarik Crnovrsanin, Shilpika, Senthil Chandrasegaran, and Kwan-Liu Ma: Staged Animation Strategies forOnline Dynamic Networks. IEEE Transactions on Visualization and Computer Graphics 27(2):539-549 (2021) [DOI]

  • Jianping Kelvin Li, Shenyu Xu, Yecong (Chris) Ye, and Kwan‐Liu Ma: Resolving Conflicting Insights inAsynchronous Collaborative Visual Analysis. Computer Graphics Forum 39(3):497-509 (2020) [DOI]

  • Takanori Fujiwara, Jian Zhao, Francine Chen, Yaoliang Yu, Kwan-Liu Ma: Interpretable Contrastive Learning for Networks. CoRR abs/2005.12419 (2020) [ArXiv]

  • Oh-Hyun Kwon and Kwan-Liu Ma: A Deep Generative Model for Graph Layout, Transactions on Visualization and Computer Graphics 26(1):665-675 (2020) (also IEEE VIS/InfoVis 2019) [DOI]

  • Joseph Kotlarek, Oh-Hyun Kwon, Kwan-Liu Ma, Peter Eades, Andreas Kerren, Karsten Klein, Falk Schreiber: A Study of Mental Maps in Immersive Network Visualization. In Proceedings of PacificVis 2020, pp. 1-10. [DOI]

  • Xu-Meng Wang, Wei Chen, Jia-Kai Chou, Chris Bryan, Huihua Guan, Wenlong Chen, Rusheng Pan, Kwan-Liu Ma: GraphProtector: A Visual Interface for Employing and Assessing Multiple Privacy Preserving Graph Algorithms. IEEE Trans. Vis. Comput. Graph. 25(1): 193-203 (2019) [DOI]

  • Annie Preston, Maksim Gomov, Kwan-Liu Ma: Uncertainty-Aware Visualization for Analyzing Heterogeneous Wildfire Detections, IEEE Computer Graphics and Applications 39(5):72-82 (2019) [DOI]

  • Keshav Dasu, Takanori Fujiwara, Kwan-Liu Ma: An Organic Visual Metaphor for Public Understanding of Conditional Co-occurrences, In Proceedings of IEEE SciVis (2018) Accepted for publication.

  • Oh-Hyun Kwon, Tarik Crnovrsanin, Kwan-Liu Ma: What Would a Graph Look Like in this Layout? A Machine Learning Approach to Large Graph Visualization. IEEE Trans. Vis. Comput. Graph. 24(1): 478-488 (2018) [DOI]

  • Fabrizio Frati, Kwan-Liu Ma: Graph Drawing and Network Visualization - 25th International Symposium, GD 2017, Boston, MA, USA, September 25-27, 2017. Lecture Notes in Computer Science 10692, Springer 2018, ISBN 978-3-319-73914-4

  • Takanori Fujiwara, Jianping Kelvin Li, Misbah Mubarak, Caitlin Ross, Christopher D. Carothers, Robert B. Ross, Kwan-Liu Ma: A visual analytics system for optimizing the performance of large-scale networks in supercomputing systems. Visual Informatics 2(1): 98-110 (2018) [DOI]

  • Robert Faris and Diane H. Felmlee: Best Friends for Now: Friendship Network Stability and Adolescents’ Life Course Goals. Social Networks and the Life Course, Frontiers in Sociology and Social Research, Volume 2. Duane Alwin, Derek Kreager, and Diane H. Felmlee. Springer (2018) [DOI]

  • Tarik Crnovrsanin, Jacqueline Chu, Kwan-Liu Ma: An Incremental Layout Method for Visualizing Online Dynamic Graphs. J. Graph Algorithms Appl. 21(1): 55-80 (2017) [DOI]

  • Alessio Arleo, Oh-Hyun Kwon, Kwan-Liu Ma: GraphRay: Distributed pathfinder network scaling. LDAV 2017: 74-83 [DOI]

Dissertations/Theses

  • Joseph Thomas Kotlarek. MS Thesis: A Study of Mental Map in Immersive Network Visualization (2019).
  • Suyun Bae. MS Thesis: A Visual Analytics Approach to Debugging Cooperative, Autonomous Multi-Robot Systems’ Worldviews (2020).
  • Takanori Fujiwara. PhD Dissertation: Advancing Visual Analytics using Dimensionality Reduction. (2021)
  • Oh-Hyun Kwon. PhD Dissertation: Machine Learning and Immersive Approaches to Graph Visualization. (2020)
  • Annie Preston. PhD Dissertation: Visualization Techniques for Transparency in Science-Based Decision Making. (2019)
  • Tarik Crnovrsanin. PhD Dissertation: Visualizing Large Complex Streaming Networks. (2019)

Software & Demos

  • This page is set up to demonstrate interactive layout selection in a latent space created with a deep generative model. [Demo]

  • This code implements a distributed algorithm to compute the Pathfinder network of a graph, as described in the paper titled "GraphRay: Distributed Pathfinder Network Scaling." [Code]

  • CLAM: This code is a high-performance implementation for computing graph layout aesthetic metrics as described in the paper titled "What Would a Graph Look Like in This Layout? A Machine Learning Approach to Large Graph Visualization." [Code]

  • This demo allows a user to explore the topological similarity between many different networks computed by our graph kernels. [Demo]

  • Organic visual metaphor for conditional co-occurrence
    https://github.com/takanori-fujiwara/organic-visual-metaphor
  • Vibrain: A Visual Analytics System for Brain Functional Connectivity Comparison across Individuals, Groups, and Time Points
    https://github.com/takanori-fujiwara/vibrain
  • Contrastive Multiple Correspondence Analysis (cMCA): Applying the Contrastive Learning Method to Identify Political Subgroups
    https://github.com/takanori-fujiwara/cmca
  • Contrastive Network Representation Learning
    https://github.com/takanori-fujiwara/cnrl
  • FEALM: Feature learning for DR using network similarity measures
    https://github.com/takanori-fujiwara/fealm
  • MulTiDR: A Visual Analytics Framework for Reviewing Multivariate Time-Series Data with Dimensionality Reduction
    https://github.com/takanori-fujiwara/multidr


Last updated, December 28, 2022