In this dissertation, we analyze the computational properties of game-theoretic centrality measures. The key idea behind game-theoretic approach to network analysis is to treat nodes as players in a cooperative game, where the value of each coalition of nodes is determined by certain graph properties. Next, the centrality of any individual node is determined by a chosen game-theoretic solution concept (notably, the Shapley value) in the same way as the payoff of a player in a cooperative game. On one hand, the advantage of game-theoretic centrality measures is that nodes are ranked not only according to their individual roles but also according to how they contribute to the role played by all possible subsets of nodes. On the other hand, the disadvantage is that the game-theoretic solution concepts are typically computationally challenging. The main contribution of this dissertation is that we show that a wide variety of game-theoretic solution concepts on networks can be computed in polynomial time. Our focus is on centralities based on the Shapley value and its various extensions, such as the Semivalues and Coalitional Semivalues. Furthermore, we prove #P-hardness of computing the Shapley value in connectivity games and propose an algorithm to compute it. Finally, we analyse computational properties of generalized version of cooperative games in which order of player matters. We propose a new representation for such games, called generalized marginal contribution networks, that allows for polynomial computation in the size of the representation of two dedicated extensions of the Shapley value to this class of games.
 This paper provides a novel algorithm to automatically detect the hidden hierarchy in terrorist networks. The algorithm is based on centrality measures used in social network analysis literature. The advantage of such automatic methods is to detect key players in terrorist networks. We illustrate the algorithm over some case studies of terrorist events that have occurred in the past. The results show great promise in detecting high value individuals. This paper looks at the difficulty in mapping covert networks. Analyzing networks after an event is fairly easy for prosecution purposes. Mapping covert networks to prevent criminal activity is much more difficult. We examine the network surrounding the tragic events of September 11th, 2001. Through public data we are able to map a portion of the network centered around the 19 dead hijackers. This map gives us some insight into the terrorist organization, yet it is incomplete. Suggestions for further work and research are offered. Abstract : The purpose of this paper is to explore the question of how modern terrorist groups manage to survive in the face of aggressive counterterrorist operations by security forces. Al Qa'ida survives to this day, despite the destruction of their Afghanistan sanctuary, the loss of countless key personnel, and continuous pressure by the United States and their allies. Why has al Qa'ida survived? Since much of the literature on terrorism focuses on how to eliminate them, this research paper focuses on why they still endure. In other words, instead of asking, "How do we kill them," this research asks, "Why don't they die?" This research employs a dynamic network analysis approach to explore the primary research question of terrorist survival. This analysis combines aspects of traditional social network analysis with a new multi-agent model that describes how terrorist groups raise agents through the organization to positions of prominence. The key to this process is the radicalization of members based on time, connectivity, and belief intensity. The testing dataset comes from the 1998 Tanzania Embassy bombing, expressed in the form of a meta-network. After four testing program iterations, the author concludes that terrorist organizational survival is based on the internal dynamics of leader selection and growth within the group as new members advance. These findings imply a number of recommendations for counterterrorist operations and intelligence activities in order to disrupt the growth and development of new leaders. Additionally, these results imply that current Joint and Army doctrine on network analysis insufficiently addresses the dynamic processes that network diagrams are intended to depict. American military counterinsurgency and counterterrorist operations can be greatly enhanced by moving from a network analysis approach based on structure to one based on dynamics.