6 papers
Orthogonal Activation with Implicit Group-Aware Bias Learning for Class Imbalance
Sukumar Kishanthan, Asela Hevapathige
Class imbalance is a common challenge in machine learning and data mining, often leading to suboptimal performance in classifiers. While deep learning excels in feature extraction,…
Beyond Fixed Depth: Adaptive Graph Neural Networks for Node Classification Under Varying Homophily
Asela Hevapathige, Asiri Wijesinghe, Ahad N. Zehmakan
Graph Neural Networks (GNNs) have achieved significant success in addressing node classification tasks. However, the effectiveness of traditional GNNs degrades on heterophilic grap…
AxelSMOTE: An Agent-Based Oversampling Algorithm for Imbalanced Classification
Sukumar Kishanthan, Asela Hevapathige
Class imbalance in machine learning poses a significant challenge, as skewed datasets often hinder performance on minority classes. Traditional oversampling techniques, which are c…
Graph Neural Diffusion via Generalized Opinion Dynamics
Asela Hevapathige, Asiri Wijesinghe, Ahad N. Zehmakan
There has been a growing interest in developing diffusion-based Graph Neural Networks (GNNs), building on the connections between message passing mechanisms in GNNs and physical di…
Depth-Adaptive Graph Neural Networks via Learnable Bakry-'Emery Curvature
Asela Hevapathige, Ahad N. Zehmakan, Qing Wang
Graph Neural Networks (GNNs) have demonstrated strong representation learning capabilities for graph-based tasks. Recent advances on GNNs leverage geometric properties, such as cur…
DeepSN: A Sheaf Neural Framework for Influence Maximization
Asela Hevapathige, Qing Wang, Ahad N. Zehmakan
Influence maximization is key topic in data mining, with broad applications in social network analysis and viral marketing. In recent years, researchers have increasingly turned to…