Jintang Li - RankMe

68 papers A* 15A 4Misc 1Journal 46Unranked 2

Year	Rank	Type	Title / Venue / Authors
2026	A*	`conf`	GT-SNT: A Linear-Time Transformer for Large-Scale Graphs via Spiking Node Tokenization. AAAI Huizhe Zhang, Jintang Li, Yuchang Zhu, Huazhen Zhong, Liang Chen
2026	J	`jnl`	SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding. IEEE Trans. Knowl. Data Eng. Yuchang Zhu, Jintang Li, Huizhe Zhang, Liang Chen, Zibin Zheng
2025	J	`jnl`	A rule-guided interpretable lightweight framework for fetal standard ultrasound plane capture and biometric measurement. Neurocomputing Jintang Li, Zhan Gao, Chunlian Wang, Bin Pu, Kenli Li
2025	Misc	`conf`	AP-Net: Semi-Supervised Ultrasound Cardiac Segmentation Using Enhanced Anatomical Prior. ICASSP Yuhuan Lu, Jintang Li, Jianxin Lin, Ying Yuan, Jagath C. Rajapakse, Ningbo Zhu, Chunlian Wang, Kenli Li
2025	J	`jnl`	Are Large Language Models In-Context Graph Learners? CoRR Jintang Li, Ruofan Wu, Yuchang Zhu, Huizhe Zhang, Liang Chen, Zibin Zheng
2025	J	`jnl`	Capturing latent evolution in dynamic graph: A dual-view architecture from spectral perspective. Knowl. Based Syst. Qianqian Chen, Jintang Li, Liang Chen
2025	J	`jnl`	FairDLA: Improving the fairness-utility trade-off in graph neural networks via dual-level alignment. Knowl. Based Syst. Ying Zhen, Yuchang Zhu, Jintang Li, Liang Chen
2025	J	`jnl`	GT-SVQ: A Linear-Time Graph Transformer for Node Classification Using Spiking Vector Quantization. CoRR Huizhe Zhang, Jintang Li, Yuchang Zhu, Liang Chen, Zibin Zheng
2025	J	`jnl`	Heterophily-Aware Representation Learning on Heterogeneous Graphs. IEEE Trans. Pattern Anal. Mach. Intell. Jintang Li, Zheng Wei, Yuchang Zhu, Ruofan Wu, Huizhe Zhang, Liang Chen, Zibin Zheng
2025	J	`jnl`	Long-term evolutionary patterns matter: Self-supervised anomaly detection on dynamic graphs. Knowl. Based Syst. Yun Fu, Che Zhou, Jintang Li, Liang Chen
2025	A*	`conf`	Measuring Diversity in Synthetic Datasets. ICML Yuchang Zhu, Huizhe Zhang, Bingzhe Wu, Jintang Li, Zibin Zheng, Peilin Zhao, Liang Chen, Yatao Bian
2025	J	`jnl`	Measuring Diversity in Synthetic Datasets. CoRR Yuchang Zhu, Huizhe Zhang, Bingzhe Wu, Jintang Li, Zibin Zheng, Peilin Zhao, Liang Chen, Yatao Bian
2025	J	`jnl`	SGNNBench: A Holistic Evaluation of Spiking Graph Neural Network on Large-scale Graph. CoRR Huizhe Zhang, Jintang Li, Yuchang Zhu, Liang Chen, Li Kuang
2025	J	`jnl`	SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding. CoRR Yuchang Zhu, Jintang Li, Huizhe Zhang, Liang Chen, Zibin Zheng
2024	A*	`conf`	A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks. ICLR Jintang Li, Huizhe Zhang, Ruofan Wu, Zulun Zhu, Baokun Wang, Changhua Meng, Zibin Zheng, Liang Chen
2024	A*	`conf`	Fair Graph Representation Learning via Sensitive Attribute Disentanglement. WWW Yuchang Zhu, Jintang Li, Zibin Zheng, Liang Chen
2024	J	`jnl`	Fair Graph Representation Learning via Sensitive Attribute Disentanglement. CoRR Yuchang Zhu, Jintang Li, Zibin Zheng, Liang Chen
2024	J	`jnl`	FairAGG: Toward Fair Graph Neural Networks via Fair Aggregation. IEEE Trans. Comput. Soc. Syst. Yuchang Zhu, Jintang Li, Liang Chen, Zibin Zheng
2024	J	`jnl`	L CoRR Xinzhou Jin, Jintang Li, Liang Chen, Chenyun Yu, Yuanzhen Xie, Tao Xie, Chengxiang Zhuo, Zang Li, Zibin Zheng
2024	A*	`conf`	One Fits All: Learning Fair Graph Neural Networks for Various Sensitive Attributes. KDD Yuchang Zhu, Jintang Li, Yatao Bian, Zibin Zheng, Liang Chen
2024	J	`jnl`	One Fits All: Learning Fair Graph Neural Networks for Various Sensitive Attributes. CoRR Yuchang Zhu, Jintang Li, Yatao Bian, Zibin Zheng, Liang Chen
2024	A	`conf`	Rethinking and Simplifying Bootstrapped Graph Latents. WSDM Wangbin Sun, Jintang Li, Liang Chen, Bingzhe Wu, Yatao Bian, Zibin Zheng
2024	A*	`conf`	Revisiting Modularity Maximization for Graph Clustering: A Contrastive Learning Perspective. KDD Yunfei Liu, Jintang Li, Yuehe Chen, Ruofan Wu, Ericbk Wang, Jing Zhou, Sheng Tian, Shuheng Shen, Xing Fu, Changhua Meng, Weiqiang Wang, Liang Chen
2024	J	`jnl`	Revisiting Modularity Maximization for Graph Clustering: A Contrastive Learning Perspective. CoRR Yunfei Liu, Jintang Li, Yuehe Chen, Ruofan Wu, Ericbk Wang, Jing Zhou, Sheng Tian, Shuheng Shen, Xing Fu, Changhua Meng, Weiqiang Wang, Liang Chen
2024	J	`jnl`	Revisiting and Benchmarking Graph Autoencoders: A Contrastive Learning Perspective. CoRR Jintang Li, Ruofan Wu, Yuchang Zhu, Huizhe Zhang, Xinzhou Jin, Guibin Zhang, Zulun Zhu, Zibin Zheng, Liang Chen
2024	J	`jnl`	SGHormer: An Energy-Saving Graph Transformer Driven by Spikes. CoRR Huizhe Zhang, Jintang Li, Liang Chen, Zibin Zheng
2024	A*	`conf`	State Space Models on Temporal Graphs: A First-Principles Study. NeurIPS Jintang Li, Ruofan Wu, Xinzhou Jin, Boqun Ma, Liang Chen, Zibin Zheng
2024	J	`jnl`	State Space Models on Temporal Graphs: A First-Principles Study. CoRR Jintang Li, Ruofan Wu, Xinzhou Jin, Boqun Ma, Liang Chen, Zibin Zheng
2024	A	`conf`	The Devil is in the Data: Learning Fair Graph Neural Networks via Partial Knowledge Distillation. WSDM Yuchang Zhu, Jintang Li, Liang Chen, Zibin Zheng
2024	A*	`conf`	Topology-monitorable Contrastive Learning on Dynamic Graphs. KDD Zulun Zhu, Kai Wang, Haoyu Liu, Jintang Li, Siqiang Luo
2023	J	`jnl`	A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks. CoRR Jintang Li, Huizhe Zhang, Ruofan Wu, Zulun Zhu, Liang Chen, Zibin Zheng, Baokun Wang, Changhua Meng
2023	J	`jnl`	Adversarial Attack on Large Scale Graph. IEEE Trans. Knowl. Data Eng. Jintang Li, Tao Xie, Liang Chen, Fenfang Xie, Xiangnan He, Zibin Zheng
2023	A*	`conf`	Enhancing Graph Collaborative Filtering via Neighborhood Structure Embedding. ICDM Xinzhou Jin, Jintang Li, Yuanzhen Xie, Liang Chen, Beibei Kong, Lei Cheng, Bo Hu, Zang Li, Zibin Zheng
2023	A	`conf`	GUARD: Graph Universal Adversarial Defense. CIKM Jintang Li, Jie Liao, Ruofan Wu, Liang Chen, Zibin Zheng, Jiawang Dan, Changhua Meng, Weiqiang Wang
2023	J	`jnl`	Hetero$^2$Net: Heterophily-aware Representation Learning on Heterogenerous Graphs. CoRR Jintang Li, Zheng Wei, Jiawang Dan, Jing Zhou, Yuchang Zhu, Ruofan Wu, Baokun Wang, Zhang Zhen, Changhua Meng, Hong Jin, Zibin Zheng, Liang Chen
2023	J	`jnl`	LasTGL: An Industrial Framework for Large-Scale Temporal Graph Learning. CoRR Jintang Li, Jiawang Dan, Ruofan Wu, Jing Zhou, Sheng Tian, Yunfei Liu, Baokun Wang, Changhua Meng, Weiqiang Wang, Yuchang Zhu, Liang Chen, Zibin Zheng
2023	J	`jnl`	Less Can Be More: Unsupervised Graph Pruning for Large-scale Dynamic Graphs. CoRR Jintang Li, Sheng Tian, Ruofan Wu, Liang Zhu, Wenlong Zhao, Changhua Meng, Liang Chen, Zibin Zheng, Hongzhi Yin
2023	J	`jnl`	Rethinking and Simplifying Bootstrapped Graph Latents. CoRR Wangbin Sun, Jintang Li, Liang Chen, Bingzhe Wu, Yatao Bian, Zibin Zheng
2023	A*	`conf`	SAD: Semi-Supervised Anomaly Detection on Dynamic Graphs. IJCAI Sheng Tian, Jihai Dong, Jintang Li, Wenlong Zhao, Xiaolong Xu, Baokun Wang, Bowen Song, Changhua Meng, Tianyi Zhang, Liang Chen
2023	J	`jnl`	SAD: Semi-Supervised Anomaly Detection on Dynamic Graphs. CoRR Sheng Tian, Jihai Dong, Jintang Li, Wenlong Zhao, Xiaolong Xu, Baokun Wang, Bowen Song, Changhua Meng, Tianyi Zhang, Liang Chen
2023	A	`conf`	SAILOR: Structural Augmentation Based Tail Node Representation Learning. CIKM Jie Liao, Jintang Li, Liang Chen, Bingzhe Wu, Yatao Bian, Zibin Zheng
2023	J	`jnl`	SAILOR: Structural Augmentation Based Tail Node Representation Learning. CoRR Jie Liao, Jintang Li, Liang Chen, Bingzhe Wu, Yatao Bian, Zibin Zheng
2023	A*	`conf`	Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks. AAAI Jintang Li, Zhouxin Yu, Zulun Zhu, Liang Chen, Qi Yu, Zibin Zheng, Sheng Tian, Ruofan Wu, Changhua Meng
2023	J	`jnl`	Scaling Up, Scaling Deep: Blockwise Graph Contrastive Learning. CoRR Jintang Li, Wangbin Sun, Ruofan Wu, Yuchang Zhu, Liang Chen, Zibin Zheng
2023	J	`jnl`	Spectral Adversarial Training for Robust Graph Neural Network. IEEE Trans. Knowl. Data Eng. Jintang Li, Jiaying Peng, Liang Chen, Zibin Zheng, Tingting Liang, Qing Ling
2023	J	`jnl`	The Devil is in the Data: Learning Fair Graph Neural Networks via Partial Knowledge Distillation. CoRR Yuchang Zhu, Jintang Li, Liang Chen, Zibin Zheng
2023	A*	`conf`	What's Behind the Mask: Understanding Masked Graph Modeling for Graph Autoencoders. KDD Jintang Li, Ruofan Wu, Wangbin Sun, Liang Chen, Sheng Tian, Liang Zhu, Changhua Meng, Zibin Zheng, Weiqiang Wang
2022	J	`jnl`	A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection. CoRR Bingzhe Wu, Jintang Li, Junchi Yu, Yatao Bian, Hengtong Zhang, Chaochao Chen, Chengbin Hou, Guoji Fu, Liang Chen, Tingyang Xu, Yu Rong, Xiaolin Zheng, Junzhou Huang, Ran He, Baoyuan Wu, Guangyu Sun, Peng Cui, Zibin Zheng, Zhe Liu, Peilin Zhao
2022	J	`jnl`	Are All Edges Necessary? A Unified Framework for Graph Purification. CoRR Zishan Gu, Jintang Li, Liang Chen
2022	J	`jnl`	GUARD: Graph Universal Adversarial Defense. CoRR Jintang Li, Jie Liao, Ruofan Wu, Liang Chen, Changhua Meng, Zibin Zheng, Weiqiang Wang
2022	J	`jnl`	Graph Enhanced Neural Interaction Model for recommendation. Knowl. Based Syst. Liang Chen, Tao Xie, Jintang Li, Zibin Zheng
2022	J	`jnl`	MaskGAE: Masked Graph Modeling Meets Graph Autoencoders. CoRR Jintang Li, Ruofan Wu, Wangbin Sun, Liang Chen, Sheng Tian, Liang Zhu, Changhua Meng, Zibin Zheng, Weiqiang Wang
2022	J	`jnl`	Neighboring Backdoor Attacks on Graph Convolutional Network. CoRR Liang Chen, Qibiao Peng, Jintang Li, Yang Liu, Jiawei Chen, Yong Li, Zibin Zheng
2022	J	`jnl`	Recent Advances in Reliable Deep Graph Learning: Adversarial Attack, Inherent Noise, and Distribution Shift. CoRR Bingzhe Wu, Jintang Li, Chengbin Hou, Guoji Fu, Yatao Bian, Liang Chen, Junzhou Huang
2022	J	`jnl`	Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks. CoRR Jintang Li, Zhouxin Yu, Zulun Zhu, Liang Chen, Qi Yu, Zibin Zheng, Sheng Tian, Ruofan Wu, Changhua Meng
2022	J	`jnl`	Spectral Adversarial Training for Robust Graph Neural Network. CoRR Jintang Li, Jiaying Peng, Liang Chen, Zibin Zheng, Tingting Liang, Qing Ling
2022	A*	`conf`	Spiking Graph Convolutional Networks. IJCAI Zulun Zhu, Jiaying Peng, Jintang Li, Liang Chen, Qi Yu, Siqiang Luo
2022	J	`jnl`	Spiking Graph Convolutional Networks. CoRR Zulun Zhu, Jiaying Peng, Jintang Li, Liang Chen, Qi Yu, Siqiang Luo
2022	A*	`conf`	Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection. KDD Bingzhe Wu, Yatao Bian, Hengtong Zhang, Jintang Li, Junchi Yu, Liang Chen, Chaochao Chen, Junzhou Huang
2022	J	`jnl`	Unifying multi-associations through hypergraph for bundle recommendation. Knowl. Based Syst. Zhouxin Yu, Jintang Li, Liang Chen, Zibin Zheng
2021	—	`conf`	GraphGallery: A Platform for Fast Benchmarking and Easy Development of Graph Neural Networks Based Intelligent Software. ICSE (Companion Volume) Jintang Li, Kun Xu, Liang Chen, Zibin Zheng, Xiao Liu
2021	J	`jnl`	GraphGallery: A Platform for Fast Benchmarking and Easy Development of Graph Neural Networks Based Intelligent Software. CoRR Jintang Li, Kun Xu, Liang Chen, Zibin Zheng, Xiao Liu
2021	J	`jnl`	Phishing Scams Detection in Ethereum Transaction Network. ACM Trans. Internet Techn. Liang Chen, Jiaying Peng, Yang Liu, Jintang Li, Fenfang Xie, Zibin Zheng
2021	A*	`conf`	Understanding Structural Vulnerability in Graph Convolutional Networks. IJCAI Liang Chen, Jintang Li, Qibiao Peng, Yang Liu, Zibin Zheng, Carl Yang
2021	J	`jnl`	Understanding Structural Vulnerability in Graph Convolutional Networks. CoRR Liang Chen, Jintang Li, Qibiao Peng, Yang Liu, Zibin Zheng, Carl Yang
2020	J	`jnl`	A Survey of Adversarial Learning on Graphs. CoRR Liang Chen, Jintang Li, Jiaying Peng, Tao Xie, Zengxu Cao, Kun Xu, Xiangnan He, Zibin Zheng
2020	J	`jnl`	Adversarial Attack on Large Scale Graph. CoRR Jintang Li, Tao Xie, Liang Chen, Fenfang Xie, Xiangnan He, Zibin Zheng
2020	—	`conf`	Deep Insights into Graph Adversarial Learning: An Empirical Study Perspective. HBAI@IJCAI Jintang Li, Zishan Gu, Qibiao Peng, Kun Xu, Liang Chen, Zibin Zheng