18 citations · 24 across the 10 of their papers we have counts for
6 papers · 1 filter
Differentiable Cluster Graph Neural Network
Yanfei Dong, Mohammed Haroon Dupty, Lambert Deng +3
Graph Neural Networks often struggle with long-range information propagation and in the presence of heterophilous neighborhoods. We address both challenges with a unified framework…
PF-GNN: Differentiable particle filtering based approximation of universal graph representations
Mohammed Haroon Dupty, Yanfei Dong, Wee Sun Lee
Message passing Graph Neural Networks (GNNs) are known to be limited in expressive power by the 1-WL color-refinement test for graph isomorphism. Other more expressive models eithe…
Factor Graph Neural Networks
Zhen Zhang, Mohammed Haroon Dupty, Fan Wu +2
In recent years, we have witnessed a surge of Graph Neural Networks (GNNs), most of which can learn powerful representations in an end-to-end fashion with great success in many rea…
Implicit Graph Neural Diffusion Networks: Convergence, Generalization, and Over-Smoothing
Guoji Fu, Mohammed Haroon Dupty, Yanfei Dong +1
Implicit Graph Neural Networks (GNNs) have achieved significant success in addressing graph learning problems recently. However, poorly designed implicit GNN layers may have limite…
Graph Representation Learning with Individualization and Refinement
Mohammed Haroon Dupty, Wee Sun Lee
Graph Neural Networks (GNNs) have emerged as prominent models for representation learning on graph structured data. GNNs follow an approach of message passing analogous to 1-dimens…
Neuralizing Efficient Higher-order Belief Propagation
Mohammed Haroon Dupty, Wee Sun Lee
Graph neural network models have been extensively used to learn node representations for graph structured data in an end-to-end setting. These models often rely on localized first…