Scalable graph synthesis with ADJ and 1 - ADJ

Jinsung Jeon, Jing Liu, Jayoung Kim, Jaehoon Lee, Noseong Park, Jamie Jooyeon Lee, Ozlem Uzuner, Sushil Jajodia

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Graph synthesis is a long-standing research problem. Many deep neural networks that learn about latent characteristics of graphs and generate fake graphs have been proposed. However, in many cases their scalability is too high to be used to synthesize large graphs. Recently, one work proposed an interesting scalable idea to learn and generate random walks that can be merged into a graph. Due to its difficulty, however, the random walk-based graph synthesis failed to show state-of-the-art performance in many cases. We present an improved random walk-based method by using negative random walks. In our experiments with 6 datasets and 8 baseline methods, our method shows the best performance in almost all cases. We achieve both high scalability and generation quality.

Original languageEnglish
Title of host publicationSIAM International Conference on Data Mining, SDM 2021
PublisherSiam Society
Pages307-315
Number of pages9
ISBN (Electronic)9781611976700
Publication statusPublished - 2021
Event2021 SIAM International Conference on Data Mining, SDM 2021 - Virtual, Online
Duration: 2021 Apr 292021 May 1

Publication series

NameSIAM International Conference on Data Mining, SDM 2021

Conference

Conference2021 SIAM International Conference on Data Mining, SDM 2021
CityVirtual, Online
Period21/4/2921/5/1

Bibliographical note

Funding Information:
Noseong Park (noseong@yonsei.ac.kr) is the corresponding author. This work was partially supported by the IITP of the Korea government (MSIT) under grant 2020-0-01361, Artificial Intelligence Graduate School Program (Yonsei University), by the Office of Naval Research under grants N00014-18-1-2670 and N00014-20-1-2407, by the Army Research Office under grant W911NF-13-1-0421, and by the National Science Foundation under grant CNS-1822094.

Publisher Copyright:
© 2021 by SIAM.

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Software

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