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Project website for NSF award: III: Medium: Collaborative Research: Human-Computer Graph Exploration and Tele-Discovery

Broader Impacts of this Project

The amount of information available to individuals today is enormous and rapidly increasing. People are constantly making sense of the world: scientists learning the literature in an unfamiliar field; analysts spotting abnormal activities in computer networks; and patients understanding their symptoms. From a user’s perspective, the main issue is not about storage, or computing power, or large scale data processing. It is more about how to best amplify his or her limited cognition power to make sense of a large data corpus via “natural” interactive exploration. This project will undertake the challenge of computer-human interactive exploration of information- rich billion-scale network datasets. These include online social networks (who is connected to whom), online auctions (who is buying what), and intelligence analysis of communication patterns and network traffic. It will blend computer-human interaction principles and decomposable visualizations with new scalable exploration techniques that are driven by information-theoretic measures. Specifically, it will design and develop a prototype system, in which users will gradually build up an understanding of billion-scale network datasets. This research could fundamentally change how people make sense of data in many domains like scientific literature, cybersecurity, and consumer decision making. The findings could increase education effectiveness, rate of scientific discovery, and enable more literate, knowledgeable, and intelligent citizens.

Research Goals

This project will combine multiple novel ideas synergistically, organized into four inter-related research thrusts:

  1. Adaptive Local Exploration using Minimum Description Length principles (MDL), KL divergence and Combinatorial Discrepancy.

  2. Pattern Tele-Discovery & Global Summarization via algorithmic teleportation tools. These will include mechanisms for querying, discovering, linking, and visualizing multi-attributed time-evolving network patterns.

  3. Scalable Data Models & Algorithms to support the interactivity demands of the previous thrusts. The proposed tools will address storage layouts via Egonet Edge Partitions and distributed sparse and persistent multidimensional sorted maps.

  4. The researchers will continually conduct multi-stage evaluations in key domains, working with real users throughout the entire development process. These will include iterative interface development via in-person user studies, virtual lab studies, and longitudinal field trials.


Atlas: Local Graph Exploration in a Global Context.
James Abello, Fred Hohman, Varun Bezzam, Duen Horng (Polo) Chau.
ACM Conference on Intelligent User Interfaces (IUI). Mar. 17-20, 2019. Los Angeles, CA, USA.

Demo video (Youtube)
Code (Github)

Scalable K-Core Decomposition for Static Graphs Using a Dynamic Graph Data Structure.
Alok Tripathy, Fred Hohman, Duen Horng (Polo) Chau, Oded Green.
IEEE International Conference on Big Data (Big Data). Dec. 10-13, 2018. Seattle, WA, USA.

VIGOR: Interactive Visual Exploration of Graph Query Results.
Robert Pienta, Fred Hohman, Alex Endert, Acar Tamersoy, Kevin Roundy, Chris Gates, Shamkant Navathe, Duen Horng (Polo) Chau.
IEEE Transactions on Visualization and Computer Graphics (Proc. VAST’17), Jan 2018.

Demo video (Vimeo)
Demo video (Youtube)

Atomic Operations for Specifying Graph Visualization Techniques.
Charles D. Stolper, Will Price, Matt Sanford, Duen Horng (Polo) Chau, John Stasko.
Poster Abstract, IEEE VIS 2017.

Carina: Interactive Million-Node Graph Visualization using Web Browser Technologies.
Dezhi (Andy) Fang, Matthew Keezer, Jacob Williams, Kshitij Kulkarni, Robert Pienta, Duen Horng (Polo) Chau.
26th International World Wide Web Conference (WWW) 2017 Companion. April 3-7, 2017. Perth, Australia.

Visual Graph Query Construction and Refinement.
Robert Pienta, Fred Hohman, Acar Tamersoy, Alex Endert, Shamkant Navathe, Hanghang Tong, Duen Horng Chau.
SIGMOD 2017. May 14-19, 2017. Chicago, IL, USA.

Best Demo, Honorable Mention

Facets: Adaptive Local Exploration of Large Graphs.
Robert Pienta, Minsuk (Brian) Kahng, Zhiyuan Lin, Jilles Vreeken, Partha Talukdar, James Abello, Ganesh Parameswaran, Duen Horng (Polo) Chau.
SIAM International Conference on Data Mining (SDM) 2017. April 27-29, 2017. Houston, Texas.

3D Exploration of Graph Layers via Vertex Cloning.
James Abello, Fred Hohman, Duen Horng Chau.
Poster Abstract, IEEE VIS 2017.

Demo video (Vimeo)
Demo video (Youtube)

Making Sense of Graph Query Results: Interactive Summarization and Exploration.
Robert Pienta, Alex Endert, Shamkant Navathe, Duen Horng (Polo) Chau.
Poster Abstract, IEEE VIS 2016. Oct 23 - 28, 2016. Baltimore, Maryland, USA.

Demo video

ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models.
Minsuk Kahng, Pierre Andrews, Aditya Kalro, Duen Horng (Polo) Chau.
IEEE Transactions on Visualization and Computer Graphics (Proc. VAST’17), Jan 2018.

Demo video (Vimeo)
Demo video (Youtube)

VISAGE: Interactive Visual Graph Querying.
Robert Pienta, Acar Tamersoy, Alex Endert, Shamkant B. Navathe, Hanghang Tong, Duen Horng (Polo) Chau
International Working Conference on Advanced Visual Interfaces (AVI 2016). June 7-10, 2016. Bari, Italy.

Demo video


Robert Pienta
Gerogia Tech, CSE. Spring, 2013 - present.
Proposal: 4/6/16. Defense: 6/27/17.
Thesis: Adaptive Visual Network Analytics: Algorithms, Interfaces, and Systems for Exploration and Querying


NSF Award Information

III: Medium: Collaborative Research: Human-computer Graph Exploration and Tele-discovery
NSF IIS 1563816 & 1563971
PIs: Duen Horng (Polo) Chau, James Abello
Funded: $1,200,000, 8/1/2016 – 7/31/2020

Project Website