2026-09-23T04:58:53Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/101227 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Koyejo, Oluwasanmi Chiu, Ping-Ko 2018-09-04T20:36:55Z 2018-09-04T20:36:55Z 2020-09-05T09:15:29Z 2018-04-25 2018-05 This thesis presents techniques of modeling large and dense networks and methods of computing distances between them. Large and dense networks arise in many disciplines. Through recent advancements in dense graph theory and graph convergence, we have a new perspective on how large graphs should be considered and how the similarity of graphs should be computed. The thesis discusses the steps to approximate the distance between graphs and the integration of a new search algorithm to accelerate computation. A software package is produced to estimate distances between graphs and made available as the Cutnorm package on PyPI. The algorithm and software shows great performance on theoretical models and is faster than existing implementations. The thesis also explores practical applications of the graph convergence theory and Cut-Distances. It presents the theory and techniques to analyze human brain connectivity graphs from the ADHD200 dataset of the 1000 Connectome Project. It also presents a new insight to monitoring Artificial Neural Network convergence during the training process. Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2020-05-01 The student, Ping-Ko Chiu, accepted the attached license on 2018-04-25 at 14:13. The student, Ping-Ko Chiu, submitted this Thesis for approval on 2018-04-25 at 14:14. This Thesis was approved for publication on 2018-04-25 at 14:59. DSpace SAF Submission Ingestion Package generated from Vireo submission #12486 on 2018-08-31 at 17:21:31 Made available in DSpace on 2018-09-04T20:36:55Z (GMT). No. of bitstreams: 2 CHIU-THESIS-2018.pdf: 1956361 bytes, checksum: 42541a8f05b185b3d2faf9949c62e68c (MD5) LICENSE.txt: 4209 bytes, checksum: f3e8b86be292b2ab84d853d94ae9bfbd (MD5) Previous issue date: 2018-04-25 Embargo set by: Seth Robbins for item 107311 Lift date: 2020-09-04T20:37:00Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system Embargo set by: Seth Robbins for item 107311 Lift date: 2020-09-04T20:42:08Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system U of I Only Restriction Lifted for Item 107311 on 2020-09-05T09:15:29Z. application/pdf http://hdl.handle.net/2142/101227 en Copyright 2018 Ping-Ko Chiu Cutnorm, Cut-Distance, Approximation Algorithm, Graph, Graph Theory, Dense Graph, Neroscience, Artificial Neural Networks Approximating cutnorm: A robust method to compute distance between dense graphs for prediction and interpretation text text Computer Science Computer Science University of Illinois at Urbana-Champaign Thesis M.S.