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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:Thesis
URI:http://hdl.handle.net/2142/102848
Rights Information:Copyright 2018 Xiyang Liu
Date Available in IDEALS:2019-02-07
Date Deposited:2018-12


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