Scholar
Fan Li
Google Scholar ID: yBr4lPIAAAAJ
University of New South Wales
Citations & Impact
Citations
56
H-index
4
i10-index
3
Publications
9
Co-authors
0
Resume (English only)
Academic Achievements
- 1. Published papers:
- - CIKM 2025: Subgraph Similarity Computation via Masked Graph Distance Network
- - IJCAI 2025: PCAN: A Pandemic-Compatible Attentive Neural Network for Retail Sales Forecasting
- - ICDE 2025: Efficient Dynamic Attributed Graph Generation
- - TKDE 2024: AdaRisk: Risk-adaptive Deep Reinforcement Learning for Vulnerable Nodes Detection
- - IJCAI 2024: Hypergraph Self-supervised Learning with Sampling-efficient Signals
- - IJCAI 2023: Fighting against Organized Fraudsters Using Risk Diffusion-based Parallel Graph Neural Network
- 2. Preprints:
- - DHG-Bench: A Comprehensive Benchmark on Deep Hypergraph Learning
- - Graph is a Natural Regularization: Revisiting Vector Quantization for Graph Representation Learning
- - HiTeC: Hierarchical Contrastive Learning on Text-Attributed Hypergraph with Semantic-Aware Augmentation
- - TCGU: Data-centric Graph Unlearning based on Transferable Condensation
- 3. Open Source Projects: DHG-Bench, the first benchmark toolkit for deep hypergraph learning
- 4. Honors and Awards: IEEE ICDE 2025 Student Travel Grant (Only 10 recipients worldwide), University International Postgraduate Award (UIPA) granted by University of New South Wales - 2024
- 5. Academic Services: Program Committee: AAAI2026, Conference Reviewer: WWW2025, KDD2025
Research Experience
- Conducting research in the Data and Knowledge Research Group (DKR) at the School of Computer Science and Engineering, University of New South Wales, focusing on Graph Mining, Hypergraph Learning, and Data-centric AI.
Education
- 1. 2023.09 - now, Ph.D Candidate, University of New South Wales, Sydney, supervised by Dr. Xiaoyang Wang and Prof. Xuemin Lin.
- 2. 2019.09 - 2023.06, Undergraduate, Tongji University, Shanghai, research intern at Fintech Lab, supervised by Prof. Dawei Cheng.
Background
- Research interests include Graph Mining, Hypergraph Learning, and Data-centric AI.
Co-authors