III: Medium: Collaborative Research: Human-Computer Graph Exploration and Tele-Discovery

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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.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 users throughout the entire development process. These will include iterative interface development via in-person user studies, virtual lab studies, and longitudinal field trials. For further information see the project web site at:http://poloclub.gatech.edu/human-computer-telediscovery/