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dc.contributor.authorPan, Longen_US
dc.date.accessioned2014-03-14T20:06:38Z
dc.date.available2014-03-14T20:06:38Z
dc.date.issued2007-12-11en_US
dc.identifier.otheretd-01072008-155049en_US
dc.identifier.urihttp://hdl.handle.net/10919/25962
dc.description.abstractPerforming social network analysis (SNA) requires a set of powerful techniques to analyze structural information contained in interactions between social entities. Many SNA technologies and methodologies have been developed and have successfully provided significant insights for small-scale interactions. However, these techniques are not suitable for analyzing large social networks, which are very popular and important in various fields and have special structural properties that cannot be obtained from small networks or their analyses. There are a number of issues that need to be further studied in the design of current SNA techniques. A number of key issues can be embodied in three fundamental and critical challenges: long processing time, large computational resource requirements, and network dynamism. In order to address these challenges, we discuss an anytime-anywhere methodology based on a parallel/distributed computational framework to effectively and efficiently analyze large and dynamic social networks. In our methodology, large social networks are decomposed into intra-related smaller parts. A coarse-level of network analysis is built based on comprehensively analyzing each part. The partial analysis results are incrementally refined over time. Also, during the analyses process, network dynamic changes are effectively and efficiently adapted based on the obtained results. In order to evaluate and validate our methodology, we implement our methodology for a set of SNA metrics which are significant for SNA applications and cover a wide range of difficulties. Through rigorous theoretical and experimental analyses, we demonstrate that our anytime-anywhere methodology isen_US
dc.publisherVirginia Techen_US
dc.relation.haspartthesis_long_pan_v2.pdfen_US
dc.rightsI hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Virginia Tech or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report.en_US
dc.subjectAnytime-Anywhere Methodologyen_US
dc.subjectParallel/Distributed Computingen_US
dc.subjectSocial Network Analysisen_US
dc.titleEffective and Efficient Methodologies for Social Network Analysisen_US
dc.typeDissertationen_US
dc.contributor.departmentComputer Scienceen_US
dc.description.degreePh. D.en_US
thesis.degree.namePh. D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineComputer Scienceen_US
dc.contributor.committeechairSantos, Eunice E.en_US
dc.contributor.committeememberSantos, Eugene Jr.en_US
dc.contributor.committeememberSotelino, Elisa D.en_US
dc.contributor.committeememberBrown, Ezra A.en_US
dc.contributor.committeememberCao, Yangen_US
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-01072008-155049/en_US
dc.date.sdate2008-01-07en_US
dc.date.rdate2008-01-16
dc.date.adate2008-01-16en_US


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