Baingana, Brian2013-09-252013-09-252013-05https://hdl.handle.net/11299/157291University of Minnesota M.S. thesis. May 2013. Major: Electrical/Computer Engineering. Advisor: Professor: Georgios B. Giannakis. 1 computer file (PDF); v, 46 pages, appendices A-B.Visual rendering of graphs is a key task in the mapping of complex network data. Although most graph drawing algorithms emphasize aesthetic appeal, certain applications such as travel-time maps place more importance on visualization of structural network properties. This thesis advocates two graph embedding approaches with centrality considerations to comply with node hierarchy. The embedding problem is formulated first as one of constrained multi-dimensional scaling (MDS), and it is solved via block coordinate descent iterations with successive approximations and guaranteed convergence to a Karush-Kuhn-Tucker (KKT) point. In addition, a regularization term enforcing graph smoothness is incorporated with the goal of reducing edge crossings. A second approach leverages the locally-linear embedding (LLE) algorithm which assumes that the graph encodes data sampled from a low-dimensional manifold. Closed-form solutions to the resulting centrality-constrained optimization problems are determined yielding meaningful embeddings. Experimental results demonstrate the efficacy of both approaches, especially for visualizing large networks on the order of thousands of nodes.en-USCoordinate descentGraph embeddingManifold learningMulti-dimensional scalingNetwork centralityNetwork visualizationThesis or Dissertation