{"513531":{"#nid":"513531","#data":{"type":"event","title":"PhD Defense by James Fairbanks","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003EDissertation Defense Announcement\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E-------------------------------------------\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003ETitle: Graph Analysis Combining Numerical, Statistical, and Streaming Techniques\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EJames Fairbanks\u003C\/p\u003E\u003Cp\u003EPhD Computational Science and Engineering\u003C\/p\u003E\u003Cp\u003ESchool of Computational Science and Engineering\u003C\/p\u003E\u003Cp\u003ECollege of Computing\u003C\/p\u003E\u003Cp\u003EGeorgia Institute of Technology\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EDate: 2016-03-28\u003C\/p\u003E\u003Cp\u003ETime: 9:00 am\u003C\/p\u003E\u003Cp\u003ELocation: Klaus Advanced Computing Building 1212\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003ECommittee:\u003C\/p\u003E\u003Cp\u003E---------------\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EProf. David Bader (Advisor, School of Computational Science and Engineering, Georgia Tech)\u003C\/p\u003E\u003Cp\u003EProf. Haesun Park (School of Computational Science and Engineering, Georgia Tech)\u003C\/p\u003E\u003Cp\u003EProf. Richard Vuduc (School of Computational Science and Engineering, Georgia Tech)\u003C\/p\u003E\u003Cp\u003EProf. Polo Chau (School of Computational Science and Engineering, Georgia Tech)\u003C\/p\u003E\u003Cp\u003EProf. Dana Randall (School of Computer Science, Georgia Tech)\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EAbstract:\u003C\/p\u003E\u003Cp\u003E------------\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EGraph analysis uses graph data collected on a physical, biological, or social\u003C\/p\u003E\u003Cp\u003Ephenomena to shed light on the underlying dynamics and behavior of the agents\u003C\/p\u003E\u003Cp\u003Ein that system. Many fields contribute to this topic including graph theory,\u003C\/p\u003E\u003Cp\u003Ealgorithms, statistics, machine learning, and linear algebra.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EThis dissertation advances a novel framework for dynamic graph analysis\u003C\/p\u003E\u003Cp\u003Ethat combines numerical, statistical, and streaming algorithms to provide deep\u003C\/p\u003E\u003Cp\u003Eunderstanding into evolving networks. For example, one can be interested in the\u003C\/p\u003E\u003Cp\u003Echanging influence structure over time. These disparate techniques each\u003C\/p\u003E\u003Cp\u003Econtribute a fragment to understanding the graph; however, their combination\u003C\/p\u003E\u003Cp\u003Eallows us to understand dynamic behavior and graph structure.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003ESpectral partitioning methods rely on eigenvectors for solving data analysis\u003C\/p\u003E\u003Cp\u003Eproblems such as clustering. Eigenvectors of large sparse systems must be\u003C\/p\u003E\u003Cp\u003Eapproximated with iterative methods. This dissertation analyzes how data\u003C\/p\u003E\u003Cp\u003Eanalysis accuracy depends on the numerical accuracy of the eigensolver. This\u003C\/p\u003E\u003Cp\u003Eleads to new bounds on the residual tolerance necessary to guarantee correct\u003C\/p\u003E\u003Cp\u003Epartitioning. We present a novel stopping criterion for spectral partitioning\u003C\/p\u003E\u003Cp\u003Eguaranteed to satisfy the Cheeger inequality along with an empirical study of\u003C\/p\u003E\u003Cp\u003Ethe performance on real world networks such as web, social, and e-commerce networks.\u003C\/p\u003E\u003Cp\u003EThis work bridges the gap between numerical analysis and computational data analysis.\u003C\/p\u003E\u003Cp\u003E \u003C\/p\u003E","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Graph Analysis Combining Numerical, Statistical, and Streaming Techniques"}],"uid":"27707","created_gmt":"2016-03-15 11:21:39","changed_gmt":"2016-10-08 02:17:03","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2016-03-28T10:00:00-04:00","event_time_end":"2016-03-28T12:00:00-04:00","event_time_end_last":"2016-03-28T12:00:00-04:00","gmt_time_start":"2016-03-28 14:00:00","gmt_time_end":"2016-03-28 16:00:00","gmt_time_end_last":"2016-03-28 16:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"100811","name":"Phd Defense"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}