Junfeng Fang

According to our database, Junfeng Fang authored at least 25 papers between 2023 and 2024.

Collaborative distances:

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

2024

Fortune favors the invariant: Enhancing GNNs' generalizability with Invariant Graph Learning.

Guibin Zhang, Yiqiao Chen, Shiyu Wang, Kun Wang, Junfeng Fang

Knowl. Based Syst., 2024

Context-DPO: Aligning Language Models for Context-Faithfulness.

Baolong Bi, Shaohan Huang, Yiwei Wang, Tianchi Yang, Zihan Zhang, Haizhen Huang, Lingrui Mei, Junfeng Fang, Zehao Li, Furu Wei, Weiwei Deng, Feng Sun, Qi Zhang, Shenghua Liu

CoRR, 2024

Cracking the Code of Hallucination in LVLMs with Vision-aware Head Divergence.

Jinghan He, Kuan Zhu, Haiyun Guo, Junfeng Fang, Zhenglin Hua, Yuheng Jia, Ming Tang, Tat-Seng Chua, Jinqiao Wang

CoRR, 2024

Optimizing Multispectral Object Detection: A Bag of Tricks and Comprehensive Benchmarks.

Chen Zhou, Peng Cheng, Junfeng Fang, Yifan Zhang, Yibo Yan, Xiaojun Jia, Yanyan Xu, Kun Wang, Xiaochun Cao

CoRR, 2024

On the Role of Attention Heads in Large Language Model Safety.

Zhenhong Zhou, Haiyang Yu, Xinghua Zhang, Rongwu Xu, Fei Huang, Kun Wang, Yang Liu, Junfeng Fang, Yongbin Li

CoRR, 2024

G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks.

Guibin Zhang, Yanwei Yue, Xiangguo Sun, Guancheng Wan, Miao Yu, Junfeng Fang, Kun Wang, Dawei Cheng

CoRR, 2024

DiffGAD: A Diffusion-based Unsupervised Graph Anomaly Detector.

Jinghan Li, Yuan Gao, Jinda Lu, Junfeng Fang, Congcong Wen, Hui Lin, Xiang Wang

CoRR, 2024

Neuron-Level Sequential Editing for Large Language Models.

Houcheng Jiang, Junfeng Fang, Tianyu Zhang, An Zhang, Ruipeng Wang, Tao Liang, Xiang Wang

CoRR, 2024

Text-guided Diffusion Model for 3D Molecule Generation.

Yanchen Luo, Junfeng Fang, Sihang Li, Zhiyuan Liu, Jiancan Wu, An Zhang, Wenjie Du, Xiang Wang

CoRR, 2024

AlphaEdit: Null-Space Constrained Knowledge Editing for Language Models.

Junfeng Fang, Houcheng Jiang, Kun Wang, Yunshan Ma, Xiang Wang, Xiangnan He, Tat-Seng Chua

CoRR, 2024

Mind Scramble: Unveiling Large Language Model Psychology Via Typoglycemia.

Miao Yu, Junyuan Mao, Guibin Zhang, Jingheng Ye, Junfeng Fang, Aoxiao Zhong, Yang Liu, Yuxuan Liang, Kun Wang, Qingsong Wen

CoRR, 2024

StruEdit: Structured Outputs Enable the Fast and Accurate Knowledge Editing for Large Language Models.

Baolong Bi, Shenghua Liu, Yiwei Wang, Lingrui Mei, Hongcheng Gao, Junfeng Fang, Xueqi Cheng

CoRR, 2024

Modeling Spatio-temporal Dynamical Systems with Neural Discrete Learning and Levels-of-Experts.

Kun Wang, Hao Wu, Guibin Zhang, Junfeng Fang, Yuxuan Liang, Yuankai Wu, Roger Zimmermann, Yang Wang

CoRR, 2024

EXGC: Bridging Efficiency and Explainability in Graph Condensation.

Junfeng Fang, Xinglin Li, Yongduo Sui, Yuan Gao, Guibin Zhang, Kun Wang, Xiang Wang, Xiangnan He

CoRR, 2024

Invariant Graph Learning for Causal Effect Estimation.

Yongduo Sui, Caizhi Tang, Zhixuan Chu, Junfeng Fang, Yuan Gao, Qing Cui, Longfei Li, Jun Zhou, Xiang Wang

Proceedings of the ACM on Web Conference 2024, 2024

Graph Anomaly Detection with Bi-level Optimization.

Yuan Gao, Junfeng Fang, Yongduo Sui, Yangyang Li, Xiang Wang, Huamin Feng, Yongdong Zhang

Proceedings of the ACM on Web Conference 2024, 2024

EXGC: Bridging Efficiency and Explainability in Graph Condensation.

Junfeng Fang, Xinglin Li, Yongduo Sui, Yuan Gao, Guibin Zhang, Kun Wang, Xiang Wang, Xiangnan He

Proceedings of the ACM on Web Conference 2024, 2024

The Heterophilic Snowflake Hypothesis: Training and Empowering GNNs for Heterophilic Graphs.

Kun Wang, Guibin Zhang, Xinnan Zhang, Junfeng Fang, Xun Wu, Guohao Li, Shirui Pan, Wei Huang, Yuxuan Liang

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

The Snowflake Hypothesis: Training and Powering GNN with One Node One Receptive Field.

Kun Wang, Guohao Li, Shilong Wang, Guibin Zhang, Kai Wang, Yang You, Junfeng Fang, Xiaojiang Peng, Yuxuan Liang, Yang Wang

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

MMGNN: A Molecular Merged Graph Neural Network for Explainable Solvation Free Energy Prediction.

Wenjie Du, Shuai Zhang, Di Wu, Jun Xia, Ziyuan Zhao, Junfeng Fang, Yang Wang

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024

Two Heads Are Better Than One: Boosting Graph Sparse Training via Semantic and Topological Awareness.

Guibin Zhang, Yanwei Yue, Kun Wang, Junfeng Fang, Yongduo Sui, Kai Wang, Yuxuan Liang, Dawei Cheng, Shirui Pan, Tianlong Chen

Proceedings of the Forty-first International Conference on Machine Learning, 2024

MolTC: Towards Molecular Relational Modeling In Language Models.

Junfeng Fang, Shuai Zhang, Chang Wu, Zhengyi Yang, Zhiyuan Liu, Sihang Li, Kun Wang, Wenjie Du, Xiang Wang

Proceedings of the Findings of the Association for Computational Linguistics, 2024

2023

Cooperative Explanations of Graph Neural Networks.

Junfeng Fang, Xiang Wang, An Zhang, Zemin Liu, Xiangnan He, Tat-Seng Chua

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, 2023

Evaluating Post-hoc Explanations for Graph Neural Networks via Robustness Analysis.

Junfeng Fang, Wei Liu, Yuan Gao, Zemin Liu, An Zhang, Xiang Wang, Xiangnan He

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Searching Lottery Tickets in Graph Neural Networks: A Dual Perspective.

Kun Wang, Yuxuan Liang, Pengkun Wang, Xu Wang, Pengfei Gu, Junfeng Fang, Yang Wang

Proceedings of the Eleventh International Conference on Learning Representations, 2023