Liang Wang
Hi! I am Liang Wang, a fourth-year PhD student at Institute of Automation, Chinese Academy of Sciences (CASIA). I am a member of NLPR and advised by Liang Wang, Shu Wu, and Qiang Liu. Previously, I received a B.E. in Software Engineering from Tongji University.
Email: liang.wang[at]cripac.ia.ac.cn
Google Scholar / Github / CV[PDF]
Research Interests
News
[Feb 14, 2025] Our preprint survey paper "Diffusion Models for Molecules: A Survey of Methods and Tasks" is released! [Paper] [Awesome Paper List] [Jan 23, 2025] One paper about molecular representation pre-training (MolSpectra) is accepted by ICLR 2025. Thanks to all collaborators! [Dec 10, 2024] One paper about knowledge graph completion (S2DN) is accepted by AAAI 2025 as oral presentation. Congratulations to Tengfei! [Dec 9, 2024] I will be attending NeurIPS 2024 from Dec 10 to Dec 15. Looking forward to seeing you in Vancouver@Canada! [Nov 1, 2024] Invited to give a talk on molecular representation learning and property prediction at DP Technology. [Slides] [Sep 26, 2024] One paper about molecular property prediction (Pin-Tuning) is accepted by NeurIPS 2024. Thanks to all collaborators!
Education
PhD (selected for the PhD honors program)
at Institute of Automation, Chinese Academy of Sciences
2021 - 2026 (Expected)
Major: Pattern Recognition and Intelligent Systems
Bachelor
at Tongji University
2017 - 2021
Major: Software Engineering
Selected Publications
(Full Publication List)AI for Science
MolSpectra: Pre-training 3D Molecular Representation with Multi-modal Energy Spectra
Liang Wang, Shaozhen Liu, Yu Rong, Deli Zhao, Qiang Liu, Shu Wu, Liang Wang
ICLR 2025
[Paper] [Code] Pin-Tuning: Parameter-Efficient In-Context Tuning for Few-Shot Molecular Property Prediction
Liang Wang, Qiang Liu, Shaozhen Liu, Xin Sun, Shu Wu, Liang Wang
NeurIPS 2024
[Paper] [Code] [Poster]
Graph Machine Learning
Rethinking Graph Masked Autoencoders through Alignment and Uniformity
Liang Wang*, Xiang Tao*, Qiang Liu, Shu Wu, Liang Wang
AAAI 2024
[Paper] [Code] [Poster] DIVE: Subgraph Disagreement for Graph Out-of-Distribution Generalization
Xin Sun, Liang Wang, Qiang Liu, Shu Wu, Zilei Wang, Liang Wang
KDD 2024
[Paper] GSLB: The Graph Structure Learning Benchmark
Zhixun Li, Liang Wang, Xin Sun, Yifan Luo, Yanqiao Zhu, Dingshuo Chen, Yingtao Luo, Xiangxin Zhou, Qiang Liu, Shu Wu, Liang Wang, Jeffrey Xu Yu
NeurIPS 2023
[Paper] [Code]
Data Mining
S2DN: Learning to Denoise Unconvincing Knowledge for Inductive Knowledge Graph Completion
Tengfei Ma, Yujie Chen, Liang Wang, Xuan Lin, Bosheng Song, Xiangxiang Zeng
AAAI 2025 (Oral)
[Paper] Bi-Level Graph Structure Learning for Next POI Recommendation
Liang Wang, Shu Wu, Qiang Liu, Yanqiao Zhu, Xiang Tao, Mengdi Zhang, Liang Wang
IEEE Transactions on Knowledge and Data Engineering 2024
[Paper] Semantic Evolvement Enhanced Graph Autoencoder for Rumor Detection
Xiang Tao, Liang Wang, Qiang Liu, Shu Wu, Liang Wang
WWW 2024
[Paper]
Projects
PyGCL
An easy-to-use library for graph contrastive learning with PyTorch. It implements a wide variety of contrastive objectives, data augmentations, contrasting modes and other utilities useful for implementing and evaluating contrastive learning on graphs.
GSLB
An open-source library built for easy implementation and evaluation of graph structure learning model family. It offers a versatile control of graph dataset loading, structure learners, structure processors, and a bunch of reproduced models.
Services
Conference Reviewer
Conference on Neural Information Processing Systems (NeurIPS), 2024, 2025 International Conference on Learning Representations (ICLR), 2025 International Conference on Machine Learning (ICML), 2025 ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2024, 2025 International Conference on Artificial Intelligence and Statistics (AISTATS), 2025
Journal Reviewer
IEEE Transactions on Knowledge and Data Engineering (TKDE)
