13 papers · 1 filter
Not Just Oversmoothing: Detecting the Echo Chamber Effect in Graph Neural Networks
Asela Hevapathige, Ahad N. Zehmakan, Asiri Wijesinghe +1
Oversmoothing is a well-known failure mode of Graph Neural Networks (GNNs). However, most existing diagnostics rely on global aggregation measures that fail to capture the heteroge…
Knowledge-Inclusive Adaptive Physics-Informed Neural Network for Microbial Interaction Modelling
Ravisha Rupasinghe, Rajith Vidanaarachchi, Asela Hevapathige +3
Physics-Informed Neural Network (PINN) is a way of including knowledge in the form of equations in Machine Learning methods. Beyond equations, knowledge exists in other forms, such…
From Specification to Architecture: A Theory Compiler for Knowledge-Guided Machine Learning
Asela Hevapathige, Yu Xia, Sachith Seneviratne +1
Theory-guided machine learning has demonstrated that including authentic domain knowledge directly into model design improves performance, sample efficiency and out-of-distribution…
Invariant-Stratified Propagation for Expressive Graph Neural Networks
Asela Hevapathige, Ahad N. Zehmakan, Asiri Wijesinghe +1
Graph Neural Networks (GNNs) face fundamental limitations in expressivity and capturing structural heterogeneity. Standard message-passing architectures are constrained by the 1-di…
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…