1 citations · 1 across the 1 of their papers we have counts for
3 papers
cs.LG2024★ 1 cited
Revisiting Edge Perturbation for Graph Neural Network in Graph Data Augmentation and Attack
Xin Liu, Yuxiang Zhang, Meng Wu +6
Edge perturbation is a basic method to modify graph structures. It can be categorized into two veins based on their effects on the performance of graph neural networks (GNNs), i.e.…
cs.AI2023
A Novel Neural-symbolic System under Statistical Relational Learning
Dongran Yu, Xueyan Liu, Shirui Pan +2
A key objective in the field of artificial intelligence is to develop cognitive models that can exhibit human-like intellectual capabilities. One promising approach to achieving th…
cs.LG2023
Towards Complex Dynamic Physics System Simulation with Graph Neural ODEs
Guangsi Shi, Daokun Zhang, Ming Jin +2
The great learning ability of deep learning models facilitates us to comprehend the real physical world, making learning to simulate complicated particle systems a promising endeav…