Aggregation Techniques to Characterize Social Networks

<p>Social network analysis focuses on modeling and understanding individuals of interest and their relationships. Aggregation of social networks can be used both to make analysis computationally easier on large networks and to gain insight in subgroup interactions. Aggregation requires determining appropriate closely knit subgroups as well as choosing a measure or measures to represent the network data. This thesis provides the analyst with several techniques for using aggregation to analyze the characteristics of social networks. The contribution of this research lies in its ability to analyze a wide variety of social network structures and available data through two methods for subgroup detection and application of two network measures. These techniques are demonstrated first on notional social networks then on open source information for the terrorist group Jema'ah Islamiyah. Since analysts rarely have perfect information of the network structure an exploration of the effects of missing arcs on subgroup detection is presented.</p><p>This work has been selected by scholars as being culturally important and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact and remains as true to the original work as possible. Therefore you will see the original copyright references library stamps (as most of these works have been housed in our most important libraries around the world) and other notations in the work.</p><p>This work is in the public domain in the United States of America and possibly other nations. Within the United States you may freely copy and distribute this work as no entity (individual or corporate) has a copyright on the body of the work.</p><p>As a reproduction of a historical artifact this work may contain missing or blurred pages poor pictures errant marks etc. Scholars believe and we concur that this work is important enough to be preserved reproduced and made generally available to the public. We appreciate your support of the preservation process and thank you for being an important part of keeping this knowledge alive and relevant.</p>