2026-09-21T07:36:54Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/120379 2023-09-05 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Tong, Hanghang 2023-05 application/pdf en text Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-05-01 The student, Zhichen Zeng, accepted the attached license on 2023-04-17 at 10:41. The student, Zhichen Zeng, submitted this Thesis for approval on 2023-04-17 at 10:55. This Thesis was approved for publication on 2023-04-17 at 16:01. DSpace SAF Submission Ingestion Package generated from Vireo submission #19008 on 2023-09-01 at 17:13:34 Position-aware regularized optimal transport for network alignment Zeng, Zhichen 2023-04-17 Network Alignment Optimal Transport Network alignment is a critical steppingstone behind a variety of multi-network mining tasks. Most of the existing methods essentially optimize a Frobenius-like distance or ranking- based loss, ignoring the underlying geometry of graph data. Optimal transport (OT), together with Wasserstein distance, has emerged to be a powerful approach accounting for the underlying geometry explicitly. Promising as it might be, the state-of-the-art OT-based alignment methods suffer from two fundamental limitations, including (1) effectiveness due to the insufficient use of topology and consistency information and (2) scalability due to the non-convex formulation and repeated computationally costly loss calculation. In this thesis, we propose a position-aware regularized optimal transport framework for network alignment named PARROT. To tackle the effectiveness issue, the proposed PARROT captures topology information by random walk with restart, with three carefully designed consistency regularization terms. To tackle the scalability issue, the regularized OT problem is decomposed into a series of convex subproblems and can be efficiently solved by the proposed constrained proximal point method with guaranteed convergence. Extensive experiments show that our algorithm achieves significant improvements in both effectiveness and scalability, outperforming the state-of-the-art network alignment methods and speeding up existing OT-based methods by up to 100 times. Thesis eng https://hdl.handle.net/2142/120379 Copyright 2023 Zhichen Zeng M.S. Thesis Computer Science University of Illinois at Urbana-Champaign Computer Science