Publications

(^# indicates that Liang is the co-first author, ^† indicates that Liang is the corresponding author)

General LLM Intelligence

Reasoning & Agents

[EMNLP 2026 Findings] Shaojie Wang, Liang Zhang^†. Knowing What to Solve Before How: Preplan Empowered LLM Mathematical Reasoning.
[ACL 2026 Findings] Shaojie Wang, Liang Zhang^†. From Implicit to Explicit: Token-Efficient Logical Supervision for Mathematical Reasoning in LLMs.

Generalize to Structured Data

[KDD 2026] Fengzhi Li, Liang Zhang^†, Yuan Zuo, Ruiqing Zhao, YanSong Liu, Yunfei Ma, Fanyu Meng, Junlan Feng. Beyond One-Size-Fits-All: Adaptive Subgraph Denoising for Zero-Shot Graph Learning with Large Language Models.
[AAAI 2026] Qiuyu Zhu, Liang Zhang^†, Qianxiong Xu, Cheng Long, Jie Zhang. SubGCache: Accelerating Graph-based RAG with Subgraph-level KV Cache.
[WSDM 2025] Qiuyu Zhu, Liang Zhang^†, Qianxiong Xu, Kaijun Liu, Cheng Long, and Xiaoyang Wang. HHGT: Hierarchical Heterogeneous Graph Transformer for Heterogeneous Graph Representation Learning.

Risk Management

[ICIS 2026] Peizhe Li, Liang Zhang^†. Beyond Isolation: LLM-Guided Hypergraph Learning across Documents and Horizons for Volatility Prediction from Conference Calls.

Operation Management

[Major Revision @ M&SOM] Xiaohui Liu, Liang Zhang, Yujing Ma, Wei Ma, and Hock-Hai Teo. The Effect of Dynamic AI Surge Pricing on Passengers' Tipping Behavior in Ride-hailing Service: An Empirical Evidence from Uber Platform.
[KDD 2026] Erdun Gao, Liang Zhang^†, Jake Fawkes, Aoqi Zuo, Wenqin Liu, Haoxuan Li, Mingming Gong, Dino Sejdinovic. Observationally Informed Adaptive Causal Experimental Design.

User Behavior Modeling

[Second Round Major Revision @ Management Science] Liang Zhang, Guannan Liu, Junjie Wu, and Yong Tan. Money May Be No Object: Modeling Reference-dependent Choices with Graph Neural Networks.
[TKDE 2024] Liang Zhang, Guannan Liu, Xiaohui Liu, and Junjie Wu. Denoising Item Graph with Disentangled Learning for Recommendation.
[SIGIR 2023] Ran Li^#, Liang Zhang^#, Guannan Liu and Junjie Wu. Next Basket Recommendation with Intent-aware Hypergraph Adversarial Network.
[TKDE 2021] Guannan Liu^#, Liang Zhang^# and Junjie Wu. Beyond Similarity: Relation-based Collaborative Filtering.
[JOMS 2018] Guannan Liu, Liang Zhang, and Baojun Ma. Risk Prediction for Product Return in Electronic Commerce Based on Random Walk.

[ICML 2025] Kaijun Liu, Sijie Ruan, Liang Zhang, Cheng Long, and Yu Liang. Generative Human Trajectory Recovery via Embedding-Space Conditional Diffusion.
[IJCNN 2025] Liang Zhang, Sijie Ruan, Kaijun Liu and Cheng Long. Cold Start Point-of-Interest Representation Learning with Conditional Diffusion Model.
[TKDD 2023] Liang Zhang, and Cheng Long. Road Network Representation Learning: A Dual Graph based Approach.
[TKDE 2022] Liang Zhang, Cheng Long and Gao Cong. Region Embedding with Intra and Inter-View Contrastive Learning.

Conference Reviewer: KDD, ICDE, VLDB, CIKM, PAKDD, ICDE, WebConf, etc.
Journal Reviewer: IJOC, TKDE, TKDD, TIST, TNNLS, Information Science, TOIS, etc.