Publications

2026

1. 

   Solving Max-Cut to Global Optimality via Feasibility-Preserving Graph Neural Networks

   Hao Chen*, Chendi Qian*, Christopher Morris, Andrea Lodi, and Can Li

   Advances in Neural Information Processing Systems, 2026

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2. 

   Learning to Approximate Uniform Facility Location via Graph Neural Networks

   Chendi Qian, Christopher Morris, Stefanie Jegelka, and Christian Sohler

   Forty-Third International Conference on Machine Learning, 2026

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3. 

   On the Expressive Power of GNNs to Solve Linear SDPs

   Chendi Qian, and Christopher Morris

   Forty-Third International Conference on Machine Learning, 2026

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4. 

   GraIP: A Benchmarking Framework For Neural Graph Inverse Problems

   Semih Cantürk, Andrei Manolache, Arman Mielke, Chendi Qian, Antoine Siraudin, Christopher Morris, Mathias Niepert, and Guy Wolf

   arXiv preprint arXiv:2601.18917, 2026

2025

1. 

   GraphBench: Next-generation graph learning benchmarking

   Timo Stoll, Chendi Qian, Ben Finkelshtein, Ali Parviz, Darius Weber, Fabrizio Frasca, Hadar Shavit, Antoine Siraudin, Arman Mielke, Marie Anastacio, and  others

   arXiv preprint arXiv:2512.04475, 2025

2. 

   Principled data augmentation for learning to solve quadratic programming problems

   Chendi Qian, and Christopher Morris

   Advances in Neural Information Processing Systems, 2025

   Spotlight

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2024

1. 

   Probabilistic Graph Rewiring via Virtual Nodes

   Chendi Qian*, Andrei Manolache*, Christopher Morris, and Mathias Niepert

   2024

   Bib HTML

   @misc{qian2024probabilistic,
     title = {Probabilistic Graph Rewiring via Virtual Nodes},
     author = {Qian*, Chendi and Manolache*, Andrei and Morris, Christopher and Niepert, Mathias},
     year = {2024},
     eprint = {2405.17311},
     archiveprefix = {arXiv},
     annotation = {* Equal contribution}
   }

2. 

   Exploring the Power of Graph Neural Networks in Solving Linear Optimization Problems

   Chendi Qian, Didier Chételat, and Christopher Morris

   In Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, 2024

   HTML PDF

2023

1. 

   Probabilistically Rewired Message-Passing Neural Networks

   Chendi Qian*, Andrei Manolache*, Kareem Ahmed, Zhe Zeng, Guy Broeck, Mathias Niepert, and Christopher Morris

   2023

   Bib HTML

   @misc{qian2023probabilistically,
     title = {Probabilistically Rewired Message-Passing Neural Networks},
     author = {Qian*, Chendi and Manolache*, Andrei and Ahmed, Kareem and Zeng, Zhe and Van den Broeck, Guy and Niepert, Mathias and Morris, Christopher},
     journal = {arXiv preprint arXiv:2310.02156},
     year = {2023},
     annotation = {* Equal contribution}
   }

2. 

   Probabilistic Task-Adaptive Graph Rewiring

   Chendi Qian, Andrei Manolache, Kareem Ahmed, Zhe Zeng, Guy Broeck, Mathias Niepert, and Christopher Morris

   In ICML 2023 Workshop on Differentiable Almost Everything: Differentiable Relaxations, Algorithms, Operators, and Simulators, 2023

   Bib HTML PDF

   @inproceedings{qian2023probabilistic,
     title = {Probabilistic Task-Adaptive Graph Rewiring},
     author = {Qian, Chendi and Manolache, Andrei and Ahmed, Kareem and Zeng, Zhe and Van den Broeck, Guy and Niepert, Mathias and Morris, Christopher},
     booktitle = {ICML 2023 Workshop on Differentiable Almost Everything: Differentiable Relaxations, Algorithms, Operators, and Simulators},
     year = {2023},
   }

3. 

   Advancing Federated Learning in 6G: A Trusted Architecture with Graph-based Analysis

   Wenxuan Ye, Chendi Qian, Xueli An, Xueqiang Yan, and Georg Carle

   In GLOBECOM 2023-2023 IEEE Global Communications Conference, 2023

   Bib HTML

   @inproceedings{ye2023advancing,
     title = {Advancing Federated Learning in 6G: A Trusted Architecture with Graph-based Analysis},
     author = {Ye, Wenxuan and Qian, Chendi and An, Xueli and Yan, Xueqiang and Carle, Georg},
     booktitle = {GLOBECOM 2023-2023 IEEE Global Communications Conference},
     pages = {56--61},
     year = {2023},
     organization = {IEEE},
   }

2022

1. 

   Influence-based mini-batching for graph neural networks

   Johannes Gasteiger*, Chendi Qian*, and Stephan Günnemann

   In Learning on Graphs Conference, 2022

   Oral presentation

   Bib HTML

   @inproceedings{gasteiger2022influence,
     title = {Influence-based mini-batching for graph neural networks},
     author = {Gasteiger*, Johannes and Qian*, Chendi and G{\"u}nnemann, Stephan},
     booktitle = {Learning on Graphs Conference},
     pages = {9--1},
     year = {2022},
     organization = {PMLR},
     note = {<span style="color:red;">Oral presentation</span>},
     annotation = {* Equal contribution}
   }

2. 

   Ordered subgraph aggregation networks

   Chendi Qian*, Gaurav Rattan*, Floris Geerts, Mathias Niepert, and Christopher Morris

   Advances in Neural Information Processing Systems, 2022

   Bib HTML

   @article{qian2022ordered,
     title = {Ordered subgraph aggregation networks},
     author = {Qian*, Chendi and Rattan*, Gaurav and Geerts, Floris and Niepert, Mathias and Morris, Christopher},
     journal = {Advances in Neural Information Processing Systems},
     volume = {35},
     pages = {21030--21045},
     year = {2022},
     annotation = {* Equal contribution}
   }