Graph matching by graph neural network

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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2019

Author dc:creator

• Liu, Xiyang

Contributors dc:contributor

• Oh, Sewoong

• Graph Neural Network, Graph Matching

Statement dc:rights

• Copyright 2018 Xiyang Liu

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/102848

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/102848