A graph similarity for deep learning

Seongmin Ok

NeurIPS 2020poster17 citations

A graph similarity for deep learning

Abstract

Graph neural networks (GNNs) have been successful in learning representations from graphs. Many popular GNNs follow the pattern of

BibTeX

@inproceedings{NEURIPS2020_0004d0b5,
 author = {Ok, Seongmin},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
 pages = {1--12},
 publisher = {Curran Associates, Inc.},
 title = {A graph similarity for deep learning},
 url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/0004d0b59e19461ff126e3a08a814c33-Paper.pdf},
 volume = {33},
 year = {2020}
}