Gang Liu

According to our database, Gang Liu authored at least 20 papers between 1997 and 2024.

Collaborative distances:

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

2024

Rationalizing Graph Neural Networks with Data Augmentation.

Gang Liu, Eric Inae, Tengfei Luo, Meng Jiang

ACM Trans. Knowl. Discov. Data, May, 2024

Learning Molecular Representation in a Cell.

Gang Liu, Srijit Seal, John Arevalo, Zhenwen Liang, Anne E. Carpenter, Meng Jiang, Shantanu Singh

CoRR, 2024

Inverse Molecular Design with Multi-Conditional Diffusion Guidance.

Gang Liu, Jiaxin Xu, Tengfei Luo, Meng Jiang

CoRR, 2024

2023

Network Immunization Strategy by Eliminating Fringe Nodes: A Percolation Perspective.

Gang Liu, Yong Deng, Kang Hao Cheong

IEEE Trans. Syst. Man Cybern. Syst., March, 2023

Graph Data Augmentation for Graph Machine Learning: A Survey.

Tong Zhao, Wei Jin, Yozen Liu, Yingheng Wang, Gang Liu, Stephan Günnemann, Neil Shah, Meng Jiang

IEEE Data Eng. Bull., 2023

Large Language Models on Graphs: A Comprehensive Survey.

Bowen Jin, Gang Liu, Chi Han, Meng Jiang, Heng Ji, Jiawei Han

CoRR, 2023

Explaining Tree Model Decisions in Natural Language for Network Intrusion Detection.

Noah Ziems, Gang Liu, John Flanagan, Meng Jiang

CoRR, 2023

Motif-aware Attribute Masking for Molecular Graph Pre-training.

Eric Inae, Gang Liu, Meng Jiang

CoRR, 2023

Data-Centric Learning from Unlabeled Graphs with Diffusion Model.

Gang Liu, Eric Inae, Tong Zhao, Jiaxin Xu, Tengfei Luo, Meng Jiang

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

Semi-Supervised Graph Imbalanced Regression.

Gang Liu, Tong Zhao, Eric Inae, Tengfei Luo, Meng Jiang

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

Explaining AI-Informed Network Intrusion Detection with Counterfactuals.

Gang Liu, Meng Jiang

Proceedings of the IEEE INFOCOM 2023, 2023

2022

On the Relationship between Counterfactual Explainer and Recommender: A Framework and Preliminary Observations.

Gang Liu, Zhihan Zhang, Zheng Ning, Meng Jiang

CoRR, 2022

Graph Data Augmentation for Graph Machine Learning: A Survey.

Tong Zhao, Gang Liu, Stephan Günnemann, Meng Jiang

CoRR, 2022

Graph Rationalization with Environment-based Augmentations.

Gang Liu, Tong Zhao, Jiaxin Xu, Tengfei Luo, Meng Jiang

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Learning from Counterfactual Links for Link Prediction.

Tong Zhao, Gang Liu, Daheng Wang, Wenhao Yu, Meng Jiang

Proceedings of the International Conference on Machine Learning, 2022

2021

Counterfactual Graph Learning for Link Prediction.

Tong Zhao, Gang Liu, Daheng Wang, Wenhao Yu, Meng Jiang

CoRR, 2021

2020

A Fuzzy Interval Time-Series Energy and Financial Forecasting Model Using Network-Based Multiple Time-Frequency Spaces and the Induced-Ordered Weighted Averaging Aggregation Operation.

Gang Liu, Fuyuan Xiao, Chin-Teng Lin, Zehong Cao

IEEE Trans. Fuzzy Syst., 2020

2019

Time Series Data Fusion Based on Evidence Theory and OWA Operator.

Gang Liu, Fuyuan Xiao

Sensors, 2019

A Data-Driven Dynamic Data Fusion Method Based on Visibility Graph and Evidence Theory.

Gang Liu, Fuyuan Xiao

IEEE Access, 2019

1997

DBMiner: a system for data mining in relational databases and data warehouses.

Jiawei Han, Jenny Chiang, Sonny Han Seng Chee, Jianping Chen, Qing Chen, Shan Cheng, Wan Gong, Micheline Kamber, Krzysztof Koperski, Gang Liu, Yijun Lu, Nebojsa Stefanovic, Lara Winstone, Betty Xia, Osmar R. Zaïane, Shuhua Zhang, Hua Zhu

Proceedings of the 1997 conference of the Centre for Advanced Studies on Collaborative Research, 1997