14 papers
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…
Detecting Differences Is Not Understanding Structure: Large Language Models Fail at Graph Isomorphism
Kumar Thushalika, Sukumar Kishanthan, Asela Hevapathige
Large language models (LLMs) have shown impressive performance on diverse reasoning tasks, yet their capacity for structural reasoning in graphs remains unclear. We investigate whe…
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…
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…
Bridging Computational Social Science and Deep Learning: Cultural Dissemination-Inspired Graph Neural Networks
Asela Hevapathige
Graph Neural Networks (GNNs) have become vital in applications like document classification in citation networks, epidemic forecasting, viral marketing, user recommendation in soci…
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…
Arch-VQ: Discrete Architecture Representation Learning with Autoregressive Priors
Deshani Geethika Poddenige, Sachith Seneviratne, Asela Hevapathige +4
Existing neural architecture representation learning methods focus on continuous representation learning, typically using Variational Autoencoders (VAEs) to map discrete architectu…
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…
Permutation-Invariant Graph Partitioning:How Graph Neural Networks Capture Structural Interactions?
Asela Hevapathige, Qing Wang
Graph Neural Networks (GNNs) have paved the way for being a cornerstone in graph-related learning tasks. Yet, the ability of GNNs to capture structural interactions within graphs r…