Yao Cheng

According to our database, Yao Cheng authored at least 19 papers between 2022 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

A survey on learning from graphs with heterophily: recent advances and future directions.

Cheng-Hua Gong, Yao Cheng, Jian-Xiang Yu, Can Xu, Cai-Hua Shan, Siqiang Luo, Xiang Li

Frontiers Comput. Sci., February, 2026

Human Cognition Inspired RAG with Knowledge Graph for Complex Problem Solving.

Yao Cheng, Yibo Zhao, Jiapeng Zhu, Yao Liu, Xing Sun, Xiang Li

Proceedings of the Fortieth AAAI Conference on Artificial Intelligence, 2026

2025

Variational Graph Autoencoder for Heterogeneous Information Networks with Missing and Inaccurate Attributes.

Yige Zhao, Jianxiang Yu, Yao Cheng, Chengcheng Yu, Yiding Liu, Xiang Li, Shuaiqiang Wang

Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, V.1, 2025

SEAGraph: Unveiling the Whole Story of Paper Review Comments.

Jianxiang Yu, Jiaqi Tan, Zichen Ding, Jiapeng Zhu, Jiahao Li, Yao Cheng, Qier Cui, Yunshi Lan, Yao Liu, Xiang Li

Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, 2025

Learning Prioritized Node-Wise Message Propagation in Graph Neural Networks (Extended Abstract).

Yao Cheng, Minjie Chen, Caihua Shan, Xiang Li

Proceedings of the 41st IEEE International Conference on Data Engineering, 2025

Can Large Language Models Act as Ensembler for Multi-GNNs?

Hanqi Duan, Yao Cheng, Jianxiang Yu, Yao Liu, Xiang Li

Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, 2025

2024

Learning Prioritized Node-Wise Message Propagation in Graph Neural Networks.

Yao Cheng, Minjie Chen, Caihua Shan, Xiang Li

IEEE Trans. Knowl. Data Eng., December, 2024

SEAGraph: Unveiling the Whole Story of Paper Review Comments.

Jianxiang Yu, Jiaqi Tan, Zichen Ding, Jiapeng Zhu, Jiahao Li, Yao Cheng, Qier Cui, Yunshi Lan, Xiang Li

CoRR, 2024

Can Large Language Models Act as Ensembler for Multi-GNNs?

Hanqi Duan, Yao Cheng, Jianxiang Yu, Xiang Li

CoRR, 2024

Boosting Graph Foundation Model from Structural Perspective.

Yao Cheng, Yige Zhao, Jianxiang Yu, Xiang Li

CoRR, 2024

Improving Graph Out-of-distribution Generalization on Real-world Data.

Can Xu, Yao Cheng, Jianxiang Yu, Haosen Wang, Jingsong Lv, Xiang Li

CoRR, 2024

Towards Learning from Graphs with Heterophily: Progress and Future.

Chenghua Gong, Yao Cheng, Jianxiang Yu, Can Xu, Caihua Shan, Siqiang Luo, Xiang Li

CoRR, 2024

Self-pro: A Self-prompt and Tuning Framework for Graph Neural Networks.

Chenghua Gong, Xiang Li, Jianxiang Yu, Yao Cheng, Jiaqi Tan, Chengcheng Yu

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2024

Resurrecting Label Propagation for Graphs with Heterophily and Label Noise.

Yao Cheng, Caihua Shan, Yifei Shen, Xiang Li, Siqiang Luo, Dongsheng Li

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

2023

Self-supervised Heterogeneous Graph Variational Autoencoders.

Yige Zhao, Jianxiang Yu, Yao Cheng, Chengcheng Yu, Yiding Liu, Xiang Li, Shuaiqiang Wang

CoRR, 2023

Prioritized Propagation in Graph Neural Networks.

Yao Cheng, Minjie Chen, Xiang Li, Caihua Shan, Ming Gao

CoRR, 2023

Label Propagation for Graph Label Noise.

Yao Cheng, Caihua Shan, Yifei Shen, Xiang Li, Siqiang Luo, Dongsheng Li

CoRR, 2023

Graph Self-Contrast Representation Learning.

Minjie Chen, Yao Cheng, Ye Wang, Xiang Li, Ming Gao

Proceedings of the IEEE International Conference on Data Mining, 2023

2022

Finding Global Homophily in Graph Neural Networks When Meeting Heterophily.

Xiang Li, Renyu Zhu, Yao Cheng, Caihua Shan, Siqiang Luo, Dongsheng Li, Weining Qian

Proceedings of the International Conference on Machine Learning, 2022