no code implementations • The Eleventh International Conference on Learning Representations 2023 • Qing Wang, Dillon Chen, Asiri Wijesinghe, Shouheng Li, Muhammad Farhan
The expressive power of Graph Neural Networks (GNNs) is fundamental for understanding their capabilities and limitations, i. e., what graph properties can or cannot be learnt by a GNN.
1 code implementation • 6 Oct 2021 • Asiri Wijesinghe, Qing Wang, Stephen Gould
This framework provides a novel optimal transport distance metric, namely Regularized Wasserstein (RW) discrepancy, which can preserve both features and structure of graphs via Wasserstein distances on features and their local variations, local barycenters and global connectivity.
1 code implementation • ICLR 2022 • Asiri Wijesinghe, Qing Wang
To elaborate this framework, we propose a novel neural model, called GraphSNN, and prove that this model is strictly more expressive than the Weisfeiler Lehman test in distinguishing graph structures.
no code implementations • 18 Dec 2020 • Jingyu Shao, Qing Wang, Asiri Wijesinghe, Erhard Rahm
Entity resolution targets at identifying records that represent the same real-world entity from one or more datasets.
1 code implementation • NeurIPS 2019 • Asiri Wijesinghe, Qing Wang
We propose a novel spectral convolutional neural network (CNN) model on graph structured data, namely Distributed Feedback-Looped Networks (DFNets).
Ranked #1 on Node Classification on NELL