Effect of Retraining Graph Generative Models with Generated Graphs-Reference-Cited by-同舟云学术

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Effect of Retraining Graph Generative Models with Generated Graphs

Published:2024-07-02 Issue: Volume:2 Page:1512-1513
ISSN:
Container-title:2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC)
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Author:

Inada Takeru¹,Tsugawa Sho²,Manada Akiko³,Watabe Kohei⁴

Affiliation:

1. Graduate School of Engineering Nagaoka University of Technology Nagaoka,Niigata,Japan

2. Institute of Systems and Information Engineering University of Tsukuba Tsukuba,Ibaraki,Japan

3. Nagaoka University of Technology Nagaoka,Department of Electrical, Electronics and Information Engineering,Niigata,Japan

4. Graduate School of Science and Engineering Saitama University Saitama,Saitama,Japan

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10633276/10633246/10633319.pdf?arnumber=10633319

Reference3 articles.

1. GraphTune: A Learning-Based Graph Generative Model With Tunable Structural Features

2. Learning Structured Output Representation using Deep Conditional Generative Models;Sohn

3. The Anatomy of a Scientific Rumor

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