Identifying Parameters for Robust Network Growth using Attachment Kernels: A case study on directed and undirected networks
Doctor of Philosophy
Network growing mechanisms are used to construct random networks that have structural behaviors similar to existing networks such as genetic networks, in efforts of understanding the evolution of complex topologies. Popular mechanisms, such as preferential attachment, are capable of preserving network features such as the degree distribution. However, little is known about such randomly grown structures regarding robustness to disturbances (e.g., edge deletions). Moreover, preferential attachment does not target optimizing the network's functionality, such as information flow. Here, we consider a network to be optimal if it's natural functionality is relatively high in addition to possessing some degree of robustness to disturbances. Specifically, a robust network would continue to (1) transmit information, (2) preserve it's connectivity and (3) preserve internal clusters post failures. In efforts to pinpoint features that would possibly replace or collaborate with the degree of a node as criteria for preferential attachment, we present a case study on both; undirected and directed networks. For undirected networks, we make a case study on wireless sensor networks in which we outline a strategy using Support Vector Regression. For Directed networks, we formulate an Integer Linear Program to gauge the exact transcriptional regulatory network optimal structures, from there on we can identify variations in structural features post optimization.
© The Author
Is Part Of
VCU University Archives
Is Part Of
VCU Theses and Dissertations
Date of Submission
I am thankful to the committee members: Dr. Vojislav Kecman, Dr. Thang Dinh and Dr. Kevin Pilkiewicz, who agreed to serve on the committee. Special thanks to Dr. Preetam Ghosh, my advisor since Fall 2011, for his continuous support and to Dr. Michael Mayo, my collaborator, for his support.
I am grateful to all my parents, brother, sister, other family members, many friends who have supported me either whole-heartedly or reluctantly. I am also thankful to the free and open source community that developed software I have been using for many years, and for the numerous online research resources that continue to help me grow as a researcher and developer.