6 citations · 7 across the 9 of their papers we have counts for
11 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…
Can Tabular In-Context Learners Generalize to Biomolecular Property Prediction?
Davy Guan, Lu Zhang, Asiri Wijesinghe +7
Predicting biomolecular properties from limited labeled data is a central bottleneck in protein engineering and small-molecule design. As strong pretrained encoders now supply rich…
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…
Flowette: Flow Matching with Graphette Priors for Graph Generation
Asiri Wijesinghe, Sevvandi Kandanaarachchi, Daniel M. Steinberg +1
We study generative modeling of graphs with recurring subgraph motifs. We propose Flowette, a continuous flow matching framework that employs a graph neural network-based transform…
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…
Amortized Active Generation of Pareto Sets
Daniel M. Steinberg, Asiri Wijesinghe, Rafael Oliveira +3
We introduce active generation of Pareto sets (A-GPS), a new framework for online discrete black-box multi-objective optimization (MOO). A-GPS learns a generative model of the Pare…
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…
Graph Self-Supervised Learning with Learnable Structural and Positional Encodings
Asiri Wijesinghe, Hao Zhu, Piotr Koniusz
Traditional Graph Self-Supervised Learning (GSSL) struggles to capture complex structural properties well. This limitation stems from two main factors: (1) the inadequacy of conven…
A Regularized Wasserstein Framework for Graph Kernels
Asiri Wijesinghe, Qing Wang, Stephen Gould
We propose a learning framework for graph kernels, which is theoretically grounded on regularizing optimal transport. This framework provides a novel optimal transport distance met…
ErGAN: Generative Adversarial Networks for Entity Resolution
Jingyu Shao, Qing Wang, Asiri Wijesinghe +1
Entity resolution targets at identifying records that represent the same real-world entity from one or more datasets. A major challenge in learning-based entity resolution is how t…
DFNets: Spectral CNNs for Graphs with Feedback-Looped Filters
Asiri Wijesinghe, Qing Wang
We propose a novel spectral convolutional neural network (CNN) model on graph structured data, namely Distributed Feedback-Looped Networks (DFNets). This model is incorporated with…