| 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 |
LIU-THESIS-2018.pdf (657kB)