{"612935":{"#nid":"612935","#data":{"type":"event","title":"PhD Defense by Kaeser M. Sabrin","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle: The Hourglass Effect in Source-Target Dependency Networks \u003C\/strong\u003E\u003Cbr \/\u003E\r\n\u003Cbr \/\u003E\r\nKaeser M. Sabrin\u003Cbr \/\u003E\r\nSchool of Computer Science\u003Cbr \/\u003E\r\nCollege of Computing\u003Cbr \/\u003E\r\nGeorgia Institute of Technology\u003Cbr \/\u003E\r\n\u003Cbr \/\u003E\r\n\u003Cstrong\u003EDate:\u003C\/strong\u003E Wednesday, Oct 31, 2018\u003Cbr \/\u003E\r\n\u003Cstrong\u003ETime:\u003C\/strong\u003E 02:30 pm - 04:30 pm\u0026nbsp;\u003Cbr \/\u003E\r\n\u003Cstrong\u003ELocation:\u003C\/strong\u003E Klaus 1202\u003Cbr \/\u003E\r\n\u003Cbr \/\u003E\r\n\u003Cstrong\u003ECommittee:\u003C\/strong\u003E\u003Cbr \/\u003E\r\nDr. Constantine Dovrolis (Advisor), School of Computer Science, Georgia Institute of Technology\u003Cbr \/\u003E\r\nDr. Bistra Dilkina, Department of Computer Science, University of Southern California \u0026amp; School of Computational Science and Engineering, Georgia Institute of Technology\u003Cbr \/\u003E\r\nDr. \u0026Uuml;mit V. \u0026Ccedil;ataly\u0026uuml;rek, School of Computational Science and Engineering, Georgia Institute of Technology\u003Cbr \/\u003E\r\nDr. Rahul Basole, School of Interactive Computing, Georgia Institute of Technology\u003Cbr \/\u003E\r\nDr. Faryad Darabi Sahneh, Department of Mathematics, University of Arizona\u003Cbr \/\u003E\r\n\u003Cbr \/\u003E\r\n\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EMany hierarchically modular systems are structured in a way that resembles the shape of an hourglass: the system generates many outputs from many inputs through a relatively small number of intermediate modules that are critical for the operation of the entire system, referred to as the waist of the hourglass.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EWe first investigate the hourglass effect in hierarchical, but not necessarily layered, dependency networks. Our analysis focuses on the number of source-to-target dependency paths that traverse each vertex. We identify the core of a dependency network as the smallest set of vertices that collectively cover a given fraction of all dependency paths. We examine if a given network exhibits the hourglass property or not, comparing its core size with a \u0026quot;flat\u0026quot; (i.e., non-hierarchical) network that preserves the source dependencies of each target in the original network.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EAs a possible explanation for the hourglass effect, we propose the \u0026quot;Reuse Preference\u0026quot; (RP) model that captures the bias of new modules to reuse intermediate modules of similar complexity instead of connecting directly to sources or low-complexity modules.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EWe have applied this analysis in dependency networks that include technological, natural and information systems, showing that they exhibit the general hourglass property but to a varying degree and with different waist characteristics. We also compare the hourglass analysis framework with existing network \u0026ldquo;core finding\u0026quot; methods and compare path centrality with other vertex centrality metrics.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EFinally, we extend our framework to networks that are not strictly hierarchical because they include feedback loops and lateral connections. In that context, we focus on the\u0026nbsp;\u003Cem\u003EC. elegans\u003C\/em\u003E\u0026nbsp;brain network (connectome) and identify a core of ten neurons that almost all paths from sensory to motor neurons traverse. We explain the role of those neurons as a dimensionality reduction mechanism, compressing the information provided by the 88 sensory neurons into a smaller set of intermediate-complexity functions that are re-used by the 119 motor neurons.\u003Cbr \/\u003E\r\n\u0026nbsp;\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"The Hourglass Effect in Source-Target Dependency Networks "}],"uid":"27707","created_gmt":"2018-10-18 12:24:43","changed_gmt":"2018-10-18 12:24:43","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2018-10-31T15:30:00-04:00","event_time_end":"2018-10-31T17:30:00-04:00","event_time_end_last":"2018-10-31T17:30:00-04:00","gmt_time_start":"2018-10-31 19:30:00","gmt_time_end":"2018-10-31 21:30:00","gmt_time_end_last":"2018-10-31 21:30:00","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":"78761","name":"Faculty\/Staff"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}