Title: | Graph matching by graph neural network |
Author(s): | Liu, Xiyang |
Advisor(s): | Oh, Sewoong |
Department / Program: | Electrical & Computer Eng |
Discipline: | Electrical & Computer Engr |
Degree Granting Institution: | University of Illinois at Urbana-Champaign |
Degree: | M.S. |
Genre: | Thesis |
Subject(s): | Graph Neural Network, Graph Matching |
Abstract: | Graph matching or network alignment refers to the problem of matching two correlated graphs. This thesis presents a deep Q learning based method, which represents the matching process by a graph neural network. By breaking the symmetry, the parameterized graph neural network is able to capture a wide range of neighborhoods. Extensive experiments on various training and testing data have shown better performance, strong scalability and the ability to adapt to different domains. |
Issue Date: | 2018-12-10 |
Type: | Text |
URI: | http://hdl.handle.net/2142/102848 |
Rights Information: | Copyright 2018 Xiyang Liu |
Date Available in IDEALS: | 2019-02-07 |
Date Deposited: | 2018-12 |