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Please use this identifier to cite or link to this item: http://hdl.handle.net/2451/14127
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dc.contributor.authorHill, Shawndra-
dc.contributor.authorAgarwal, Deepak-
dc.contributor.authorBell, Robert-
dc.contributor.authorVolinsky, Chris-
dc.date.accessioned2005-11-10T14:35:07Z-
dc.date.available2005-11-10T14:35:07Z-
dc.date.issued2005-02-19-
dc.identifier.urihttp://hdl.handle.net/2451/14127-
dc.description.abstractA dynamic network is a special type of network which is comprised of connected transactors which have repeated evolving interaction. Data on large dynamic networks such as telecommunications networks and the Internet are pervasive. However, representing dynamic networks in a manner that is conducive to efficient large-scale analysis is a challenge. In this paper, we represent dynamic graphs using a data structure introduced by Cortes et. a]. [Q]. We advocate their representation because it accounts for the evolution of relationships between transactors through time, mitigates noise at the local transactor level, and allows for the removal of stale relationships. Our work improves on their heuristic arguments by formalizing the representation with three tunable parameters. In doing this, we develop a generic framework for evaluating and tuning any dynamic graph. We show that the storage saving approximations involved in the representation do not affect predictive performance, and typically improve it. We motivate our approach using a fraud detection example from the telecommunications industry, and demonstrate that we can outperform published results on the fraud detection task. In addition, we present preliminary analysis on web logs and email networks.en
dc.format.extent6234247 bytes-
dc.format.mimetypeapplication/pdf-
dc.languageEnglishEN
dc.language.isoen_US-
dc.publisherStern School of Business, New York Universityen
dc.relation.ispartofseriesCeDER-05-11-
dc.subjectapproximate subgraphsen
dc.subjectdynamic graphsen
dc.subjectexponential averagingen
dc.subjectfraud detectionen
dc.subjecttransactional data streamsen
dc.titleBuilding an Effective Representation for Dynamic Networksen
dc.typeWorking Paperen
dc.description.seriesInformation Systems Working Papers SeriesEN
Appears in Collections:CeDER Working Papers
IOMS: Information Systems Working Papers

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