Thesis (M. A.)--University of Hong Kong, 2006. / Also available in print.

Thesis (M.B.A.)--University of Hong Kong, 1998. / Includes bibliographical references.

Thèse--Paris. / Description based on print version record.

Thesis (Ph. D.)--Washington State University, May 2009. / Title from PDF title page (viewed on Apr. 26, 2010). "College of Business." Includes bibliographical references (p. 170-189).

Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 122-134).

Thesis (M.A.)--Villanova University, 2006. / Political Science Dept. Includes bibliographical references.

Thesis (Ph.D.)--University of Texas at Dallas, 2007. / Includes vita. Includes bibliographical references (leaves 93-100)

Texte remanié de: Diss.--Philosophische Fakultät--Frankfurt am Main--Johann Wolfgang Goethe-Universität, 1963. / Bibliogr. p. VII-XVII. Notes bibliogr. Index.

Any reasonably large group of individuals, families, states, and parties exhibits the phenomenon of subgroup formations within the group such that the members of each group have a strong connection or bonding between each other. The reasons of the formation of these subgroups that we call alliances differ in different situations, such as, kinship and friendship (in the case of individuals), common economic interests (for both individuals and states), common political interests, and geographical proximity. This structure of alliances is not only prevalent in social networks, but it is also an important characteristic of similarity networks of natural and unnatural objects. (A similarity network defines the links between two objects based on their similarities). Discovery of such structure in a data set is called clustering or unsupervised learning and the ability to do it automatically is desirable for many applications in the areas of pattern recognition, computer vision, artificial intelligence, behavioral and social sciences, life sciences, earth sciences, medicine, and information theory. In this dissertation, we study a graph theoretical model of alliances where an alliance of the vertices of a graph is a set of vertices in the graph, such that every vertex in the set is adjacent to equal or more vertices inside the set than the vertices outside it. We study the problem of partitioning a graph into alliances and identify classes of graphs that have such a partition. We present results on the relationship between the existence of such a partition and other well known graph parameters, such as connectivity, subgraph structure, and degrees of vertices. We also present results on the computational complexity of finding such a partition. An alliance cover set is a set of vertices in a graph that contains at least one vertex from every alliance of the graph. The complement of an alliance cover set is an alliance free set, that is, a set that does not contain any alliance as a subset. We study the properties of these sets and present tight bounds on their cardinalities. In addition, we also characterize the graphs that can be partitioned into alliance free and alliance cover sets. Finally, we present an approximate algorithm to discover alliances in a given graph. At each step, the algorithm finds a partition of the vertices into two alliances such that the alliances are strongest among all such partitions. The strength of an alliance is defined as a real number p, such that every vertex in the alliance has at least p times more neighbors in the set than its total number of neighbors in the graph). We evaluate the performance of the proposed algorithm on standard data sets.

Alliances continue to remain fundamental at the core of international politics. How states make their alliance choices is important for international order and security. The end of the Cold War brought ethnic conflicts and political instabilities in the Balkan region. Based on its size and geographical configuration, Romania again confronted its history in the alliance dilemma of whether to "balance" against threatening states or to "bandwagon" with the states that posed the greatest threats in order to appease their power. Stephen M. Walt (1987) predicted that in a case like that of Romania, the statesmen would most likely choose to bandwagon because of two motives: (i) for "defensive" purposes in order to maintain its independence in the face of a potential threat and (ii) for "offensive" reasons in order to acquire territory. After reviewing Romanian historical records on alliances since 1878, the evidence is compelling that the case of Romania conforms to Walt's (1987) theory only to the extent that "balancing is not universal." Thus, it depends on which perspective balancing or bandwagoning is considered from in forming alliances. Romania either formed alliances to balance threats, or allied with the threat. Territorial security was one of the most recurrent motives prevalent in Romanian historical records that prompted Romania to form alliances. As expected, Walt's (1987) last three factors did not provide a great explanation for Romanian alliances. Modest support was found for ideological solidarity,but little for economic ideology and transnational political penetration. / Master of Arts