Yang Liu （柳阳）
Scholar

Yang Liu （柳阳）

Google Scholar ID: kVoIIXkAAAAJ
Assistant Professor @ Institute of Computing Technology, Chinese Academy of Sciences
Graph Neural NetworkFinancial Data MiningFraud Detection
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Citations & Impact
All-time
Citations
1,485
 
H-index
14
 
i10-index
16
 
Publications
20
 
Co-authors
0
 
Contact
Emailliuyang2023@ict.ac.cnGitHubOpen ↗
Publications
20 items
Browse publications on Google Scholar (top-right) ↗
Resume (English only)
Academic Achievements
  • Published multiple papers, including:
  • - ICAIF 2025: Robust Graph Learning in Finance
  • - ICDM 2025: Dilution of Unreliable Information: Learning in Graph with Noisy Structures and Absent Attributes
  • - KDD 2025: GRASP: Differentially Private Graph Reconstruction Defense with Structured Perturbation
  • - WWW 2025: SPEAR: A Structure-Preserving Manipulation Method for Graph Backdoor Attacks, Panoramic Interests: Stylistic-Content Aware Personalized Headline Generation
  • - AAAI 2025: Dynamic Graph Learning with Static Relations for Credit Risk Assessment
  • - DASFAA 2025: OFTEN: Graph Invariant Learning via Soft Environment Inference
  • - ICASSP 2025: Domain-aware Node Representation Learning for Graph Out-of-Distribution Generalization
  • - WSDM 2025: LOGIN: A Large Language Model Consulted Graph Neural Network Training Framework
  • - ICLR 2024: Boosting the Adversarial Robustness of Graph Neural Networks: An OOD Perspective
  • - ICLR 2023: Revisiting Graph Adversarial Attack and Defense From a Data Distribution Perspective
  • - KDD 2023: FLOOD: A Flexible Invariant Learning Framework for Out-of-Distribution Generalization on Graphs
  • - KDD 2022: UD-GNN: Uncertainty-aware Debiased Training on Semi-Homophilous Graphs
  • - WWW 2021: Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection
  • - CIKM 2020: Alike and Unlike: Resolving Class Imbalance Problem in Financial Credit Risk Assessment
Research Experience
  • From Feb 2022 to Feb 2023, he was a visiting scholar in the NExT Research Centre, National University of Singapore (NUS), advised by Prof. Chua Tat-Seng, and worked with Prof. Fuli Feng and Prof. Yunshan Ma.
Education
  • Received B.S. degree in Mathematics from Nanjing University (NJU) in 2017; obtained PhD from Institute of Computing Technology, Chinese Academy of Sciences, supervised by Prof. Qing He and co-supervised by Prof. Xiang Ao.
Background
  • Research interests include graph machine learning and AI safety. Published 20+ papers at top international AI conferences such as WWW, ICLR, KDD.
Co-authors
0 total
Co-authors: 0 (list not available)