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dc.contributor.advisorChau, Duen Horng (Polo)
dc.contributor.authorPienta, Robert S.
dc.date.accessioned2018-01-22T21:10:05Z
dc.date.available2018-01-22T21:10:05Z
dc.date.created2017-12
dc.date.issued2017-10-04
dc.date.submittedDecember 2017
dc.identifier.urihttp://hdl.handle.net/1853/59220
dc.description.abstractLarge 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.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technology
dc.subjectVisual querying
dc.subjectVisual graph querying
dc.subjectGraph querying
dc.subjectSubgraph matching
dc.subjectApproximate subgraph matching
dc.subjectGraph querying
dc.subjectGraph exploration
dc.subjectGraph navigation
dc.subjectGraph foraging
dc.subjectGraph sensemaking
dc.subjectSubgraph Embedding
dc.subjectGraph Embedding
dc.subjectDimensionality reduction
dc.subjectVisual analytics
dc.subjectVisualization
dc.subjectGraph visualization
dc.titleAdaptive visual network analytics: Algorithms, interfaces, and systems for exploration and querying
dc.typeDissertation
dc.description.degreePh.D.
dc.contributor.departmentComputational Science and Engineering
thesis.degree.levelDoctoral
dc.contributor.committeeMemberNavathe, Shamkant
dc.contributor.committeeMemberAbello, James
dc.contributor.committeeMemberVreeken, Jilles
dc.contributor.committeeMemberTong, Hanghang
dc.contributor.committeeMemberDilkina, Bistra
dc.contributor.committeeMemberEndert, Alex
dc.date.updated2018-01-22T21:10:05Z


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