1 code implementation • 10 Mar 2023 • Hamed Shirzad, Ameya Velingker, Balaji Venkatachalam, Danica J. Sutherland, Ali Kemal Sinop
We show that incorporating Exphormer into the recently-proposed GraphGPS framework produces models with competitive empirical results on a wide variety of graph datasets, including state-of-the-art results on three datasets.
Ranked #1 on Graph Classification on MNIST
1 code implementation • 13 Jun 2022 • Hamed Shirzad, Kaveh Hassani, Danica J. Sutherland
A wide range of models have been proposed for Graph Generative Models, necessitating effective methods to evaluate their quality.
no code implementations • 20 Jun 2021 • Hamed Shirzad, Hossein Hajimirsadeghi, Amir H. Abdi, Greg Mori
We propose TD-GEN, a graph generation framework based on tree decomposition, and introduce a reduced upper bound on the maximum number of decisions needed for graph generation.