1 citations · 1 across the 7 of their papers we have counts for
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SGS-GNN: A Supervised Graph Sparsification method for Graph Neural Networks
Siddhartha Shankar Das, Naheed Anjum Arafat, Muftiqur Rahman +3
We propose SGS-GNN, a novel supervised graph sparsifier that learns the sampling probability distribution of edges and samples sparse subgraphs of a user-specified size to reduce t…
When Witnesses Defend: A Witness Graph Topological Layer for Adversarial Graph Learning
Naheed Anjum Arafat, Debabrota Basu, Yulia Gel +1
Capitalizing on the intuitive premise that shape characteristics are more robust to perturbations, we bridge adversarial graph learning with the emerging tools from computational t…
Finite Volume Features, Global Geometry Representations, and Residual Training for Deep Learning-based CFD Simulation
Loh Sher En Jessica, Naheed Anjum Arafat, Wei Xian Lim +2
Computational fluid dynamics (CFD) simulation is an irreplaceable modelling step in many engineering designs, but it is often computationally expensive. Some graph neural network (…