Position-aware regularized optimal transport for network alignment
Zeng, Zhichen
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Permalink
https://hdl.handle.net/2142/120379
Description
Title
Position-aware regularized optimal transport for network alignment
Author(s)
Zeng, Zhichen
Issue Date
2023-04-17
Director of Research (if dissertation) or Advisor (if thesis)
Tong, Hanghang
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
network alignment
optimal transport
Abstract
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.
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