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Title: Effectiveness of Unique Grouping Techniques for Network Nodes in Serving Various Application Domains
Author: Chen, Alan
Advisor: Alhajj, Reda
Keywords: Artificial Intelligence;Computer Science
Issue Date: 29-Apr-2013
Abstract: A network is an abstract representation of entities, which can be objects or concepts. Entities are generally represented by nodes, and connected to other entities in the model by links based on their relationship or interaction with the other entities. Networks are a simple but powerful tool for modeling and analyzing relationships between entities, which have become an important technique in many different fields of study. The semantics of the nodes and the links are determined based on the specific domain of study. Nodes in a network could be classified into groups. A group in a network is a subset of the nodes in the network that is being considered together for certain functions. Grouping network nodes refers to a technique of assigning labels to the nodes; grouping techniques are important for building an understanding of the network, and can be used in solving many problems in various domains. Various techniques have been explored to group network nodes together, such that nodes in each group are highly connected, and nodes between groups have fewer connections. General grouping techniques will discover these high density groups in a wide variety of networks for further examination in numerous fields. The problem with general grouping techniques is that they are multipurpose tools, thus they produce groups of nodes with some characteristics that are commonly sought. Nevertheless, there may be situations that call for discovering groups that have an unusual characteristic. In these problems, a unique grouping technique that is designed specifically to address that particular problem would be a much more effective means to solve the problem. Accordingly, a general framework is proposed in this thesis to help guide the design of unique grouping techniques. This thesis will demonstrate the effectiveness and significance of unique grouping techniques through the development, and application of unique grouping techniques in four distinctive cases. This thesis will show that unique grouping techniques should be a serious consideration alongside general grouping techniques for research work dealing with networks.
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