@article{li2024subgraphrag,title={Simple is Effective: The Roles of Graphs and Large Language Models in Knowledge-Graph-Based Retrieval-Augmented Generation},author={Li, Mufei and Miao, Siqi and Li, Pan},journal={arXiv preprint arXiv:2410.20724},year={2024},note={Mufei Li and Siqi Miao contributed equally to this work.},}
arXiv
LayerDAG: A Layerwise Autoregressive Diffusion Model for Directed Acyclic Graph Generation
Mufei Li, Viraj Shitole, Eli Chien, Changhai Man, Zhaodong Wang, Srinivas Sridharan, Ying Zhang, Tushar Krishna, and Pan Li
@article{li2024layerdag,title={Layer{DAG}: A Layerwise Autoregressive Diffusion Model for Directed Acyclic Graph Generation},author={Li, Mufei and Shitole, Viraj and Chien, Eli and Man, Changhai and Wang, Zhaodong and Sridharan, Srinivas and Zhang, Ying and Krishna, Tushar and Li, Pan},journal={arXiv preprint arXiv:2411.02322},year={2024},}
Nat Rev Electr Eng
Opportunities and challenges of graph neural networks in electrical engineering
Eli Chien, Mufei Li, Anthony Aportela, Kerr Ding, Shuyi Jia, Supriyo Maji, Zhongyuan Zhao, Javier Duarte, Victor Fung, Cong Hao, Yunan Luo, Olgica Milenkovic, David Pan, Santiago Segarra, and Pan Li
@article{chien2024opportunities,author={Chien, Eli and Li, Mufei and Aportela, Anthony and Ding, Kerr and Jia, Shuyi and Maji, Supriyo and Zhao, Zhongyuan and Duarte, Javier and Fung, Victor and Hao, Cong and Luo, Yunan and Milenkovic, Olgica and Pan, David and Segarra, Santiago and Li, Pan},doi={10.1038/s44287-024-00076-z},journal={Nature Reviews Electrical Engineering},number={8},pages={529--546},title={Opportunities and challenges of graph neural networks in electrical engineering},url={https://doi.org/10.1038/s44287-024-00076-z},volume={1},year={2024}}
TMLR
GraphMaker: Can Diffusion Models Generate Large Attributed Graphs?
Mufei Li, Eleonora Kreačić, Vamsi K. Potluru, and Pan Li
@article{li2024graphmaker,title={GraphMaker: Can Diffusion Models Generate Large Attributed Graphs?},author={Li, Mufei and Kreačić, Eleonora and Potluru, Vamsi K. and Li, Pan},year={2024},journal={Transactions on Machine Learning Research},github_stars={Graph-COM/GraphMaker},}
2023
2022
Mol. Inf.
Benchmarking Accuracy and Generalizability of Four Graph Neural Networks Using Large In Vitro ADME Datasets from Different Chemical Spaces
Fabio Broccatelli, Richard Trager, Michael Reutlinger, George Karypis, and Mufei Li
@article{https://doi.org/10.1002/minf.202100321,title={Benchmarking Accuracy and Generalizability of Four Graph Neural Networks Using Large In Vitro ADME Datasets from Different Chemical Spaces},author={Broccatelli, Fabio and Trager, Richard and Reutlinger, Michael and Karypis, George and Li, Mufei},year={2022},journal={Molecular Informatics},volume={41},number={8},pages={2100321},doi={10.1002/minf.202100321},}
Sci. Rep.
CTKG: A Knowledge Graph for Clinical Trials
Ziqi Chen, Bo Peng, Vasileios Ioannidis, Mufei Li, George Karypis, and Xia Ning
@article{CTKG,title={CTKG: A Knowledge Graph for Clinical Trials},author={Chen, Ziqi and Peng, Bo and Ioannidis, Vasileios and Li, Mufei and Karypis, George and Ning, Xia},year={2022},journal={Scientific Reports},volume={12},number={1},doi={10.1038/s41598-022-08454-z},}
2021
Brief. Bioinform.
De novo generation of dual-target ligands using adversarial training and reinforcement learning
@article{DLGN,title={De novo generation of dual-target ligands using adversarial training and reinforcement learning},author={Lu, Fengqing and Li, Mufei and Min, Xiaoping and Li, Chunyan and Zeng, Xiangxiang},year={2021},journal={Briefings in Bioinformatics},volume={22},issue={6},doi={10.1093/bib/bbab333},}
ACS Omega
DGL-LifeSci: An Open-Source Toolkit for Deep Learning on Graphs in Life Science
Mufei Li, Jinjing Zhou, Jiajing Hu, Wenxuan Fan, Yangkang Zhang, Yaxin Gu, and George Karypis
@article{dgllife,title={DGL-LifeSci: An Open-Source Toolkit for Deep Learning on Graphs in Life Science},author={Li, Mufei and Zhou, Jinjing and Hu, Jiajing and Fan, Wenxuan and Zhang, Yangkang and Gu, Yaxin and Karypis, George},year={2021},journal={ACS Omega},volume={6},number={41},pages={27233-27238},doi={10.1021/acsomega.1c04017},github_stars={awslabs/dgl-lifesci},dimensions={false},}
2020
DRKG - Drug Repurposing Knowledge Graph for Covid-19
Vassilis N. Ioannidis, Xiang Song, Saurav Manchanda, Mufei Li, Xiaoqin Pan, Da Zheng, Xia Ning, Xiangxiang Zeng, and George Karypis
@misc{drkg2020,title={DRKG - Drug Repurposing Knowledge Graph for Covid-19},author={Ioannidis, Vassilis N. and Song, Xiang and Manchanda, Saurav and Li, Mufei and Pan, Xiaoqin and Zheng, Da and Ning, Xia and Zeng, Xiangxiang and Karypis, George},year={2020},github_stars={gnn4dr/DRKG}}
2019
arXiv
Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks
Minjie Wang, Da Zheng, Zihao Ye, Quan Gan, Mufei Li, Xiang Song, Jinjing Zhou, Chao Ma, Lingfan Yu, Yu Gai, Tianjun Xiao, Tong He, George Karypis, Jinyang Li, and Zheng Zhang
@article{wang2019dgl,title={Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks},author={Wang, Minjie and Zheng, Da and Ye, Zihao and Gan, Quan and Li, Mufei and Song, Xiang and Zhou, Jinjing and Ma, Chao and Yu, Lingfan and Gai, Yu and Xiao, Tianjun and He, Tong and Karypis, George and Li, Jinyang and Zhang, Zheng},year={2019},journal={arXiv preprint arXiv:1909.01315},github_stars={dmlc/dgl},}
ICLR workshop
A statistical characterization of attentions in graph neural networks
@article{GAT_stats,title={A statistical characterization of attentions in graph neural networks},author={Li, Mufei and Zhang, Hao and Shi, Xingjian and Wang, Minjie and Zhang, Zheng},year={2019},journal={International Conference on Learning Representations (ICLR) 2019 Workshop on Representation Learning on Graphs and Manifolds},}