A Framework for the Static and Dynamic Analysis of Interaction Graphs
A Framework for the Static and Dynamic Analysis of Interaction Graphs DISSERTATION Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Sitaram Asur, B.E., M.Sc. * * * * * The Ohio State University 2009 Dissertation Committee: Approved by Prof. Srinivasan Parthasarathy, Adviser Prof. Gagan Agrawal Adviser Prof. P. Sadayappan Graduate Program in Computer Science and Engineering c Copyright by Sitaram Asur 2009 ABSTRACT Data originating from many different real-world domains can be represented mean- ingfully as interaction networks. Examples abound, ranging from gene expression networks to social networks, and from the World Wide Web to protein-protein inter- action networks. The study of these complex networks can result in the discovery of meaningful patterns and can potentially afford insight into the structure, properties and behavior of these networks. Hence, there is a need to design suitable algorithms to extract or infer meaningful information from these networks. However, the challenges involved are daunting. First, most of these real-world networks have specific topological constraints that make the task of extracting useful patterns using traditional data mining techniques difficult. Additionally, these networks can be noisy (containing unreliable interac- tions), which makes the process of knowledge discovery difficult. Second, these net- works are usually dynamic in nature. Identifying the portions of the network that are changing, characterizing and modeling the evolution, and inferring or predict- ing future trends are critical challenges that need to be addressed in the context of understanding the evolutionary behavior of such networks. To address these challenges, we propose a framework of algorithms designed to detect, analyze and reason about the structure, behavior and evolution of real-world interaction networks.
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