1 code implementation • journal 2023 • Nanni, L., Fusaro, D., Fantozzi, C., Pretto, A.
We release with this paper the open-source implementation of our method.
Ranked #4 on Camouflaged Object Segmentation on CAMO (MAE metric, using extra training data)
1 code implementation • Proceedings of the AAAI Conference on Artificial Intelligence 2023 • Zeng, D., Liu, Chen, W., Zhou, L., Zhang, M., & Qu, H
Despite the great achievements of Graph Neural Networks (GNNs) in graph learning, conventional GNNs struggle to break through the upper limit of the expressiveness of first-order Weisfeiler-Leman graph isomorphism test algorithm (1-WL) due to the consistency of the propagation paradigm of GNNs with the 1-WL. Based on the fact that it is easier to distinguish the original graph through subgraphs, we propose a novel framework neural network framework called Substructure Aware Graph Neural Networks (SAGNN) to address these issues.
Ranked #7 on Graph Regression on ZINC