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- W164680383 abstract "Various phenomena within the information systems discipline can be studied using the social network paradigm that views social entities as nodes with links between them. The social network analysis (SNA) theory has applications in knowledge management, computer mediated communications, security informatics, and other domains. Challenges in SNA can be classified into three broad areas: instance modeling, evolution modeling, and predictive modeling. Instance modeling focuses on the study of static network properties, evolution modeling examines factors behind network growth, and predictive modeling is concerned with identification of hidden and future network links. This dissertation presents four essays that address these challenges with empirical studies in knowledge management and security informatics. The first essay on instance and evolution modeling contributes to SNA theory by examining a real-world network that contains interactions between thirty thousand individuals. The study is among the few that empirically examine large human-only networks and verify the presence of small-world properties and scale-free distributions. In addition, it proposes a novel application of a network evolution model to examine the growth of networks across geographical boundaries. The second essay on evolution modeling proposes a methodology to identify" @default.
- W164680383 created "2016-06-24" @default.
- W164680383 creator A5044713455 @default.
- W164680383 date "2008-01-01" @default.
- W164680383 modified "2023-09-26" @default.
- W164680383 title "Instance, Evolution, and Predictive Modeling of Social Networks" @default.
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