Yixin Liu

According to our database, Yixin Liu authored at least 36 papers between 2021 and 2024.

Collaborative distances:

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

2024

Integrating Graphs With Large Language Models: Methods and Prospects.

Shirui Pan, Yizhen Zheng, Yixin Liu

IEEE Intell. Syst., 2024

Unifying Unsupervised Graph-Level Anomaly Detection and Out-of-Distribution Detection: A Benchmark.

Yili Wang, Yixin Liu, Xu Shen, Chenyu Li, Kaize Ding, Rui Miao, Ying Wang, Shirui Pan, Xin Wang

CoRR, 2024

ARC: A Generalist Graph Anomaly Detector with In-Context Learning.

Yixin Liu, Shiyuan Li, Yu Zheng, Qingfeng Chen, Chengqi Zhang, Shirui Pan

CoRR, 2024

Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models.

Yixin Liu, Kai Zhang, Yuan Li, Zhiling Yan, Chujie Gao, Ruoxi Chen, Zhengqing Yuan, Yue Huang, Hanchi Sun, Jianfeng Gao, Lifang He, Lichao Sun

CoRR, 2024

TrustLLM: Trustworthiness in Large Language Models.

Lichao Sun, Yue Huang, Haoran Wang, Siyuan Wu, Qihui Zhang, Chujie Gao, Yixin Huang, Wenhan Lyu, Yixuan Zhang, Xiner Li, Zhengliang Liu, Yixin Liu, Yijue Wang, Zhikun Zhang, Bhavya Kailkhura, Caiming Xiong, Chaowei Xiao, Chunyuan Li, Eric P. Xing, Furong Huang, Hao Liu, Heng Ji, Hongyi Wang, Huan Zhang, Huaxiu Yao, Manolis Kellis, Marinka Zitnik, Meng Jiang, Mohit Bansal, James Zou, Jian Pei, Jian Liu, Jianfeng Gao, Jiawei Han, Jieyu Zhao, Jiliang Tang, Jindong Wang, John Mitchell, Kai Shu, Kaidi Xu, Kai-Wei Chang, Lifang He, Lifu Huang, Michael Backes, Neil Zhenqiang Gong, Philip S. Yu, Pin-Yu Chen, Quanquan Gu, Ran Xu, Rex Ying, Shuiwang Ji, Suman Jana, Tianlong Chen, Tianming Liu, Tianyi Zhou, William Wang, Xiang Li, Xiangliang Zhang, Xiao Wang, Xing Xie, Xun Chen, Xuyu Wang, Yan Liu, Yanfang Ye, Yinzhi Cao, Yue Zhao

CoRR, 2024

GOODAT: Towards Test-Time Graph Out-of-Distribution Detection.

Luzhi Wang, Dongxiao He, He Zhang, Yixin Liu, Wenjie Wang, Shirui Pan, Di Jin, Tat-Seng Chua

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection.

Yu Zheng, Ming Jin, Yixin Liu, Lianhua Chi, Khoa Tran Phan, Yi-Ping Phoebe Chen

IEEE Trans. Knowl. Data Eng., December, 2023

Anomaly Detection in Dynamic Graphs via Transformer.

Yixin Liu, Shirui Pan, Yu Guang Wang, Fei Xiong, Liang Wang, Qingfeng Chen, Vincent Cheng-Siong Lee

IEEE Trans. Knowl. Data Eng., December, 2023

Graph Self-Supervised Learning: A Survey.

Yixin Liu, Ming Jin, Shirui Pan, Chuan Zhou, Yu Zheng, Feng Xia, Philip S. Yu

IEEE Trans. Knowl. Data Eng., June, 2023

Improving Faithfulness for Vision Transformers.

Lijie Hu, Yixin Liu, Ninghao Liu, Mengdi Huai, Lichao Sun, Di Wang

CoRR, 2023

Stable Unlearnable Example: Enhancing the Robustness of Unlearnable Examples via Stable Error-Minimizing Noise.

Yixin Liu, Kaidi Xu, Xun Chen, Lichao Sun

CoRR, 2023

GraphCloak: Safeguarding Task-specific Knowledge within Graph-structured Data from Unauthorized Exploitation.

Yixin Liu, Chenrui Fan, Xun Chen, Pan Zhou, Lichao Sun

CoRR, 2023

MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Use.

Yue Huang, Jiawen Shi, Yuan Li, Chenrui Fan, Siyuan Wu, Qihui Zhang, Yixin Liu, Pan Zhou, Yao Wan, Neil Zhenqiang Gong, Lichao Sun

CoRR, 2023

Towards Data-centric Graph Machine Learning: Review and Outlook.

Xin Zheng, Yixin Liu, Zhifeng Bao, Meng Fang, Xia Hu, Alan Wee-Chung Liew, Shirui Pan

CoRR, 2023

BiomedGPT: A Unified and Generalist Biomedical Generative Pre-trained Transformer for Vision, Language, and Multimodal Tasks.

Kai Zhang, Jun Yu, Zhiling Yan, Yixin Liu, Eashan Adhikarla, Sunyang Fu, Xun Chen, Chen Chen, Yuyin Zhou, Xiang Li, Lifang He, Brian D. Davison, Quanzheng Li, Yong Chen, Hongfang Liu, Lichao Sun

CoRR, 2023

Watermarking Text Data on Large Language Models for Dataset Copyright Protection.

Yixin Liu, Hongsheng Hu, Xuyun Zhang, Lichao Sun

CoRR, 2023

BadGPT: Exploring Security Vulnerabilities of ChatGPT via Backdoor Attacks to InstructGPT.

Jiawen Shi, Yixin Liu, Pan Zhou, Lichao Sun

CoRR, 2023

A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT.

Yihan Cao, Siyu Li, Yixin Liu, Zhiling Yan, Yutong Dai, Philip S. Yu, Lichao Sun

CoRR, 2023

Unlearnable Graph: Protecting Graphs from Unauthorized Exploitation.

Yixin Liu, Chenrui Fan, Pan Zhou, Lichao Sun

CoRR, 2023

Securing Biomedical Images from Unauthorized Training with Anti-Learning Perturbation.

Yixin Liu, Haohui Ye, Kai Zhang, Lichao Sun

CoRR, 2023

A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT.

Ce Zhou, Qian Li, Chen Li, Jun Yu, Yixin Liu, Guangjing Wang, Kai Zhang, Cheng Ji, Qiben Yan, Lifang He, Hao Peng, Jianxin Li, Jia Wu, Ziwei Liu, Pengtao Xie, Caiming Xiong, Jian Pei, Philip S. Yu, Lichao Sun

CoRR, 2023

Backdoor Attacks to Pre-trained Unified Foundation Models.

Zenghui Yuan, Yixin Liu, Kai Zhang, Pan Zhou, Lichao Sun

CoRR, 2023

GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection.

Yixin Liu, Kaize Ding, Huan Liu, Shirui Pan

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

Towards Self-Interpretable Graph-Level Anomaly Detection.

Yixin Liu, Kaize Ding, Qinghua Lu, Fuyi Li, Leo Yu Zhang, Shirui Pan

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

Learning Strong Graph Neural Networks with Weak Information.

Yixin Liu, Kaize Ding, Jianling Wang, Vincent C. S. Lee, Huan Liu, Shirui Pan

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

PREM: A Simple Yet Effective Approach for Node-Level Graph Anomaly Detection.

Junjun Pan, Yixin Liu, Yizhen Zheng, Shirui Pan

Proceedings of the IEEE International Conference on Data Mining, 2023

Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating.

Yixin Liu, Yizhen Zheng, Daokun Zhang, Vincent C. S. Lee, Shirui Pan

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

SEAT: Stable and Explainable Attention.

Lijie Hu, Yixin Liu, Ninghao Liu, Mengdi Huai, Lichao Sun, Di Wang

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Cyclic label propagation for graph semi-supervised learning.

Zhao Li, Yixin Liu, Zhen Zhang, Shirui Pan, Jianliang Gao, Jiajun Bu

World Wide Web, 2022

Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning.

Yixin Liu, Zhao Li, Shirui Pan, Chen Gong, Chuan Zhou, George Karypis

IEEE Trans. Neural Networks Learn. Syst., 2022

Graph Neural Networks for Graphs with Heterophily: A Survey.

Xin Zheng, Yixin Liu, Shirui Pan, Miao Zhang, Di Jin, Philip S. Yu

CoRR, 2022

From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach.

Yu Zheng, Ming Jin, Yixin Liu, Lianhua Chi, Khoa Tran Phan, Shirui Pan, Yi-Ping Phoebe Chen

CoRR, 2022

Towards Unsupervised Deep Graph Structure Learning.

Yixin Liu, Yu Zheng, Daokun Zhang, Hongxu Chen, Hao Peng, Shirui Pan

Proceedings of the WWW '22: The ACM Web Conference 2022, Virtual Event, Lyon, France, April 25, 2022

2021

Anomaly Detection in Dynamic Graphs via Transformer.

Yixin Liu, Shirui Pan, Yu Guang Wang, Fei Xiong, Liang Wang, Vincent C. S. Lee

CoRR, 2021

Graph Self-Supervised Learning: A Survey.

Yixin Liu, Shirui Pan, Ming Jin, Chuan Zhou, Feng Xia, Philip S. Yu

CoRR, 2021

ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning.

Ming Jin, Yixin Liu, Yu Zheng, Lianhua Chi, Yuan-Fang Li, Shirui Pan

Proceedings of the CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1, 2021