Title:
Adaptive visual network analytics: Algorithms, interfaces, and systems for exploration and querying

dc.contributor.advisor Chau, Duen Horng
dc.contributor.author Pienta, Robert S.
dc.contributor.committeeMember Navathe, Shamkant
dc.contributor.committeeMember Abello, James
dc.contributor.committeeMember Vreeken, Jilles
dc.contributor.committeeMember Tong, Hanghang
dc.contributor.committeeMember Dilkina, Bistra
dc.contributor.committeeMember Endert, Alex
dc.contributor.department Computational Science and Engineering
dc.date.accessioned 2018-01-22T21:10:05Z
dc.date.available 2018-01-22T21:10:05Z
dc.date.created 2017-12
dc.date.issued 2017-10-04
dc.date.submitted December 2017
dc.date.updated 2018-01-22T21:10:05Z
dc.description.abstract Large graphs are now commonplace, amplifying the fundamental challenges of exploring, navigating, and understanding massive data. Our work tackles critical aspects of graph sensemaking, to create human-in-the-loop network exploration tools. This dissertation is comprised of three research thrusts, in which we combine techniques from data mining, visual analytics, and graph databases to create scalable, adaptive, interaction-driven graph sensemaking tools. (1) Adaptive Local Graph Exploration: our FACETS system introduces an adaptive exploration paradigm for large graphs to guide user towards interesting and surprising content, based on a novel measurement of surprise and subjective user interest using feature-entropy and the Jensen-Shannon divergence. (2) Interactive Graph Querying: VISAGE empowers analysts to create and refine queries in a visual, interactive environment, without having to write in a graph querying language, outperforming conventional query writing and refinement. Our MAGE algorithm locates high quality approximate subgraph matches and scales to large graphs. (3) Summarizing Subgraph Discovery: we introduce VIGOR, a novel system for summarizing graph querying results, providing practical tools and addressing research challenges in interpreting, grouping, comparing, and exploring querying results. This dissertation contributes to visual analytics, data mining, and their intersection through: interactive systems and scalable algorithms; new measures for ranking content; and exploration paradigms that overcome fundamental challenges in visual analytics. Our contributions work synergistically by utilizing the strengths of visual analytics and graph data mining together to forward graph analytics.
dc.description.degree Ph.D.
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/59220
dc.language.iso en_US
dc.publisher Georgia Institute of Technology
dc.subject Visual querying
dc.subject Visual graph querying
dc.subject Graph querying
dc.subject Subgraph matching
dc.subject Approximate subgraph matching
dc.subject Graph querying
dc.subject Graph exploration
dc.subject Graph navigation
dc.subject Graph foraging
dc.subject Graph sensemaking
dc.subject Subgraph Embedding
dc.subject Graph Embedding
dc.subject Dimensionality reduction
dc.subject Visual analytics
dc.subject Visualization
dc.subject Graph visualization
dc.title Adaptive visual network analytics: Algorithms, interfaces, and systems for exploration and querying
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.advisor Chau, Duen Horng
local.contributor.corporatename College of Computing
local.contributor.corporatename School of Computational Science and Engineering
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relation.isOrgUnitOfPublication c8892b3c-8db6-4b7b-a33a-1b67f7db2021
relation.isOrgUnitOfPublication 01ab2ef1-c6da-49c9-be98-fbd1d840d2b1
thesis.degree.level Doctoral
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