{"152431":{"#nid":"152431","#data":{"type":"news","title":"Who\u2019s the Most Influential in a Social Graph?","body":[{"value":"\u003Cp\u003EAt an airport, many people are essential for planes to take off. Gate staffs, refueling crews, flight attendants and pilots are in constant communication with each other as they perform required tasks. But it\u2019s the air traffic controller who talks with every plane, coordinating departures and runways. Communication must run through her in order for an airport to run smoothly and safely.\u003C\/p\u003E\u003Cp\u003EIn computational terms, the air traffic controller is the \u201cbetweenness centrality,\u201d the most connected person in the system. In this example, finding the key influencer is easy because each departure process is nearly the same.\u003C\/p\u003E\u003Cp\u003EDetermining the most influential person on a social media network (or, in computer terms, a graph) is more complex. Thousands of users are interacting about a single subject at the same time. New people (known computationally as edges) are constantly joining the streaming conversation.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EGeorgia Tech has developed a new algorithm that quickly determines betweenness centrality for streaming graphs. The algorithm can identify influencers as information changes within a network. The first-of-its-kind streaming tool was presented this week by Computational Science and Engineering Ph.D. candidate Oded Green at the Social Computing Conference in Amsterdam.\u003C\/p\u003E\u003Cp\u003E\u201cUnlike existing algorithms, our system doesn\u2019t restart the computational process from scratch each time a new edge is inserted into a graph,\u201d said College of Computing Professor David Bader, the project\u2019s leader. \u201cRather than starting over, our algorithm stores the graph\u2019s prior centrality data and only does the bare minimal computations affected by the inserted edges.\u201d\u003C\/p\u003E\u003Cp\u003EIn some cases, betweenness centrality can be computed more than 100 times faster using the Georgia Tech software. The open source software will soon be available to businesses.\u003C\/p\u003E\u003Cp\u003EBader, the Institute\u2019s executive director for high performance computing, says the technology has wide-ranging applications. For instance, advertisers could use the software to identify which celebrities are most influential on Twitter or Facebook, or both, during product launches.\u003C\/p\u003E\u003Cp\u003E\u201cDespite a fragmented social media landscape, data analysts would be able to use the algorithm to look at each social media network and mark inferences about a single influencer across these different platforms,\u201d said Bader.\u003C\/p\u003E\u003Cp\u003EAs another example, the algorithm could be used for traffic patterns during a wreck or traffic jam. Transportation officials could quickly determine the best new routes based on gradual side-street congestion.\u003C\/p\u003E\u003Cp\u003EThe accepted paper was co-authored by Electrical and Computer Engineering Ph.D. candidate Rob McColl.\u003C\/p\u003E\u003Cp\u003E\u003Cem\u003EThis project is supported by the National Science Foundation (NSF) (Award Number CNS-0708307). The content is solely the responsibility of the principal investigators and does not necessarily represent the official views of the NSF. \u003C\/em\u003E\u003C\/p\u003E","summary":null,"format":"limited_html"}],"field_subtitle":[{"value":"New Georgia Tech Software Recognizes Key Influencers Faster Than Ever"}],"field_summary":[{"value":"\u003Cp\u003EGeorgia Tech has developed a new algorithm that quickly determines betweenness centrality for streaming graphs. The algorithm can identify influencers as information changes within a network. The first-of-its-kind streaming tool was presented this week by Computational Science and Engineering Ph.D. candidate Oded Green at the Social Computing Conference in Amsterdam.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Georgia Tech has developed a new algorithm that quickly determines key influencers on social media."}],"uid":"27560","created_gmt":"2012-09-07 10:50:39","changed_gmt":"2016-10-08 03:12:47","author":"Jason Maderer","boilerplate_text":"","field_publication":"","field_article_url":"","dateline":{"date":"2012-09-07T00:00:00-04:00","iso_date":"2012-09-07T00:00:00-04:00","tz":"America\/New_York"},"extras":[],"related_links":[{"url":"http:\/\/www.cc.gatech.edu\/~bader\/","title":"High-Performance Computing"},{"url":"http:\/\/www.cc.gatech.edu\/","title":"College of Computing"},{"url":"http:\/\/www.cse.gatech.edu\/","title":"School of Computational Science and Engineering"}],"groups":[{"id":"1183","name":"Home"}],"categories":[{"id":"153","name":"Computer Science\/Information Technology and Security"},{"id":"135","name":"Research"}],"keywords":[{"id":"13255","name":"david bader"},{"id":"167543","name":"social media"}],"core_research_areas":[{"id":"39431","name":"Data Engineering and Science"}],"news_room_topics":[],"event_categories":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003EJason Maderer\u003Cbr \/\u003EMedia Relations\u003Cbr \/\u003E\u003Ca href=\u0022mailto:maderer@gatech.edu\u0022\u003Emaderer@gatech.edu\u003C\/a\u003E\u003Cbr \/\u003E404-385-2966\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"email":["maderer@gatech.edu"],"slides":[],"orientation":[],"userdata":""}}}