no code implementations • 4 Feb 2025 • Chendi Qian, Christopher Morris
Recently, message-passing graph neural networks (MPNNs) have shown potential for solving combinatorial and continuous optimization problems due to their ability to capture variable-constraint interactions.
1 code implementation • 27 May 2024 • Chendi Qian, Andrei Manolache, Christopher Morris, Mathias Niepert
Message-passing graph neural networks (MPNNs) have emerged as a powerful paradigm for graph-based machine learning.
1 code implementation • 16 Oct 2023 • Chendi Qian, Didier Chételat, Christopher Morris
Recently, machine learning, particularly message-passing graph neural networks (MPNNs), has gained traction in enhancing exact optimization algorithms.
1 code implementation • 3 Oct 2023 • Chendi Qian, Andrei Manolache, Kareem Ahmed, Zhe Zeng, Guy Van Den Broeck, Mathias Niepert, Christopher Morris
Message-passing graph neural networks (MPNNs) emerged as powerful tools for processing graph-structured input.
no code implementations • 11 Sep 2023 • Wenxuan Ye, Chendi Qian, Xueli An, Xueqiang Yan, Georg Carle
Second, given the distributed nature and graph structure between clients and nodes in the pre-processing layer, GNN is leveraged to identify abnormal local models, enhancing system security.
no code implementations • 18 Dec 2022 • Johannes Gasteiger, Chendi Qian, Stephan Günnemann
Using graph neural networks for large graphs is challenging since there is no clear way of constructing mini-batches.
no code implementations • 22 Jun 2022 • Chendi Qian, Gaurav Rattan, Floris Geerts, Christopher Morris, Mathias Niepert
Numerous subgraph-enhanced graph neural networks (GNNs) have emerged recently, provably boosting the expressive power of standard (message-passing) GNNs.
no code implementations • 29 Sep 2021 • Johannes Klicpera, Chendi Qian, Stephan Günnemann
Training graph neural networks on large graphs is challenging since there is no clear way of how to extract mini batches from connected data.