1 code implementation • 20 Feb 2024 • Nithin Chalapathi, Yiheng Du, Aditi Krishnapriyan
Our approach imposes the constraint over smaller decomposed domains, each of which is solved by an "expert" through differentiable optimization.
1 code implementation • 16 Jan 2024 • Bohang Zhang, Jingchu Gai, Yiheng Du, Qiwei Ye, Di He, LiWei Wang
Specifically, we identify a fundamental expressivity measure termed homomorphism expressivity, which quantifies the ability of GNN models to count graphs under homomorphism.
1 code implementation • 8 Dec 2023 • Yiheng Du, Nithin Chalapathi, Aditi Krishnapriyan
In contrast to current machine learning approaches which enforce PDE constraints by minimizing the numerical quadrature of the residuals in the spatiotemporal domain, we leverage Parseval's identity and introduce a new training strategy through a \textit{spectral loss}.
1 code implementation • 14 Feb 2023 • Bohang Zhang, Guhao Feng, Yiheng Du, Di He, LiWei Wang
Recently, subgraph GNNs have emerged as an important direction for developing expressive graph neural networks (GNNs).
Ranked #1 on Subgraph Counting - C6 on Synthetic Graph