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dc.contributor.authorElhajj, Ahmad*
dc.contributor.authorElsheikh, A.*
dc.contributor.authorAddam, O.*
dc.contributor.authorAlzohbi, M.*
dc.contributor.authorZarour, O.*
dc.contributor.authorAksaç, A.*
dc.contributor.authorÖztürk, O.*
dc.contributor.authorÖzyer, T.*
dc.contributor.authorRidley, Mick J.*
dc.contributor.authorAlhajj, R.*
dc.date.accessioned2017-11-01T13:08:16Z
dc.date.available2017-11-01T13:08:16Z
dc.date.issued2013
dc.identifier.citationElhajj A, Elsheikh A, Addam O, Alzohbi M, Zarour O, Aksaç A, Öztürk O, Özyer T, Ridley M and Alhajj R (2013) Estimating the Importance of Terrorists in a Terror Network. In: Özyer T, Erdem Z, Rokne J and Khoury S (eds) Mining Social Networks and Security Informatics. Lecture Notes in Social Networks. Springer, Dordrecht. pp 267-283.en_US
dc.identifier.urihttp://hdl.handle.net/10454/13601
dc.descriptionnoen_US
dc.description.abstractWhile criminals may start their activities at individual level, the same is in general not true for terrorists who are mostly organized in well established networks. The effectiveness of a terror network could be realized by watching many factors, including the volume of activities accomplished by its members, the capabilities of its members to hide, and the ability of the network to grow and to maintain its influence even after the loss of some members, even leaders. Social network analysis, data mining and machine learning techniques could play important role in measuring the effectiveness of a network in general and in particular a terror network in support of the work presented in this chapter. We present a framework that employs clustering, frequent pattern mining and some social network analysis measures to determine the effectiveness of a network. The clustering and frequent pattern mining techniques start with the adjacency matrix of the network. For clustering, we utilize entries in the table by considering each row as an object and each column as a feature. Thus features of a network member are his/her direct neighbors. We maintain the weight of links in case of weighted network links. For frequent pattern mining, we consider each row of the adjacency matrix as a transaction and each column as an item. Further, we map entries into a 0/1 scale such that every entry whose value is greater than zero is assigned the value one; entries keep the value zero otherwise. This way we can apply frequent pattern mining algorithms to determine the most influential members in a network as well as the effect of removing some members or even links between members of a network. We also investigate the effect of adding some links between members. The target is to study how the various members in the network change role as the network evolves. This is measured by applying some social network analysis measures on the network at each stage during the development. We report some interesting results related to two benchmark networks: the first is 9/11 and the second is Madrid bombing.en_US
dc.language.isoenen_US
dc.subjectSocial network analysis; Terror networks; Data mining; Data analysis; Knowledge discovery; Machine learningen_US
dc.titleEstimating the Importance of Terrorists in a Terror Networken_US
dc.status.refereedyesen_US
dc.typeBook chapteren_US
dc.type.versionNo full-text in the repositoryen_US
dc.identifier.doihttps://doi.org/10.1007/978-94-007-6359-3_14


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