Selected Publications (Google Scholar Profile):

👨‍💻✨ Our Research Works are Open-Sourced – Explore Them on Our Lab GitHub Repository  🚀

(In chronological order, * indicates corresponding author, + indicates supervised student)

In the Year of 2024:

• [KDD'2024] "HiGPT: Heterogenous Graph Language Models"

J. Tang+, Y. Yang, W. Wei, L. Shi, L. Xia, D. Yin and C. Huang*

ACM International Conference on Knowledge Discovery and Data Mining

[paper] [code] [media]

• [KDD'2024] "UrbanGPT: Spatio-Temporal Large Language Models"

Z. Li+, L. Xia, J. Tang, Y. Xu, L. Shi, L. Xia, D. Yin and C. Huang*

ACM International Conference on Knowledge Discovery and Data Mining

[paper] [code] [media]

• [SIGIR'2024] "GraphGPT: Graph Instruction Tuning for Large Language Models"

J. Tang+, Y. Yang, W. Wei, L. Shi, L. Su, S. Cheng, D. Yin and C. Huang*

ACM Conference on Research and Development in Information Retrieval

🌟 (Top-2 Most Cited Paper: 2 / 159 Accepted Papers) 🌟

[paper] [code] [media]

• [SIGIR'2024] "SelfGNN: Self-Supervised Graph Neural Networks for Sequential Recommendation"

Y. Liu+, L. Xia and C. Huang*

ACM Conference on Research and Development in Information Retrieval

[paper] [code]

• [ICML'2024] "FlashST: A Simple and Universal Prompt-Tuning Framework for Traffic Prediction"

Z. Li+, L. Xia, Y. Xu and C. Huang*

International Conference on Machine Learning

[paper] [code] [media]

• [WWW'2024] "RLMRec: Representation Learning with Large Language Models for Recommendation"

X. Ren+, W. Wei, L. Xia, L. Su, S. Cheng, J. Wang, D. Yin and C. Huang*

ACM The Web Conference

🌟 (Top-1 Most Cited Paper: 1 / 405 Accepted Papers) 🌟

[paper] [code] [media]

• [WWW'2024] "GraphPro: Graph Pre-training and Prompt Learning for Recommendation"

Y. Yang+, L. Xia, D. Luo, K. Lin and C. Huang*

ACM The Web Conference

[paper] [code] [media]

• [WWW'2024] "PromptMM: Multi-Modal Knowledge Distillation for Recommendation with Prompt-Tuning"

W. Wei+, J. Tang, Y. Jiang, L. Xia and C. Huang*

ACM The Web Conference

[paper] [code] [media]

• [WSDM'2024] "LLMRec: Large Language Models with Graph Augmentation for Recommendation"

W. Wei+, X. Ren, J. Tang, Q. Wang, L. Su, S. Cheng, J. Wang, D. Yin and C. Huang*

ACM International Conference on Web Search and Data Mining

🌟 (Top-1 Most Cited Paper: 1 / 112 Accepted Papers) 🌟

[paper] [code] [media]

• [WSDM'2024] "SSLRec: A Self-Supervised Learning Framework for Recommendation"

X. Ren+, L. Xia, Y. Yang, W. Wei, T. Wang, X. Cai and C. Huang*

ACM International Conference on Web Search and Data Mining

[paper] [code] [media]

• [WSDM'2024] "DiffKG: Knowledge Graph Diffusion Model for Recommendation"

Y. Jiang+, Y. Yang, L. Xia and C. Huang*

ACM International Conference on Web Search and Data Mining

[paper] [code] [media]

In the Year of 2023:

• [WWW'2023] "Automated Self-Supervised Learning for Recommendation"

L. Xia+, C. Huang*, C. Huang, K. Lin, T. Yu and B. Kao

ACM The Web Conference

🌟 (Spotlight & Best Paper Nomination: 16/323 Accepted Papers) 🌟

[paper] [code] [media]

• [WWW'2023] "Multi-Modal Self-Supervised Learning for Recommendation"

W. Wei+, C. Huang*, L. Xia and C. Zhang

ACM The Web Conference

🌟 (Most Influential Papers of WWW'2023 - Rank 3rd / 323 Accepted Papers) 🌟

[paper] [code] [media]

• [WWW'2023] "Debiased Contrastive Learning for Sequential Recommendation"

Y. Yang+, C. Huang*, L. Xia, C. Huang, D. Luo and K. Li

ACM The Web Conference

🌟 (Top-5 Most Cited Paper: 5 / 323 Accepted Papers) 🌟

[paper] [code] [media]

• [WWW'2023] "Automated Spatio-Temporal Graph Contrastive Learning"

Q. Zhang+, C. Huang*, L. Xia, Z. Wang, Z. Li and S. Yiu

ACM The Web Conference

[paper] [code] [media]

• [WWW'23] "Graph-less Collaborative Filtering"

L. Xia+, C. Huang*, J. Shi and Y. Xu

ACM The Web Conference

[paper] [code] [media]

• [ICLR'2023] "LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation"

X. Cai+, C. Huang*, L. Xia and X. Ren

International Conference on Learning Representations

🌟 (Selected as Spotlight Paper: 25%) 🌟

[paper] [code] [media]

• [WSDM'2023] "Heterogeneous Graph Contrastive Learning for Recommendation"

M. Chen+, C. Huang*, L. Xia,  W. Wei, Y. Xu and R. Luo

ACM International Conference on Web Search and Data Mining

🌟 (Top-2 Most Cited Paper: 2 / 123 Accepted Papers) 🌟

[paper] [code] [media]

• [KDD'2023] "Knowledge Graph Self-Supervised Rationalization for Recommendation"

Y. Yang+, C. Huang*, L. Xia and C. Huang

ACM International Conference on Knowledge Discovery and Data Mining

[paper] [code]

• [KDD'2023] "Adaptive Graph Contrastive Learning for Recommendation"

Y. Jiang+, C. Huang* and L. Xia

ACM International Conference on Knowledge Discovery and Data Mining

[paper] [code] [media]

• [NeurIPS'2023] "GPT-ST: Generative Pre-Training of Spatio-Temporal Graph Neural Networks"

Z. Li+, L. Xia, Y. Xu and C. Huang*

International Conference on Neural Information Processing Systems

[paper] [code] [media]

• [NeurIPS'2023] "LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting"

X. Liu, Y. Xia, Y. Liang, J. Hu, Y. Wang, L. Bai, C. Huang, Z. Liu, B. Hooi, R. Zimmermann 

International Conference on Neural Information Processing Systems

[paper] [code]

• [CIKM'2023] "Explainable Spatio-Temporal Graph Neural Networks"

J. Tang+, L. Xia and C. Huang*

ACM International Conference on Information and Knowledge Management

[paper] [code]

• [CIKM'2023] "Spatio-Temporal Meta Contrastive Learning"

J. Tang+, L. Xia, J. Hu and C. Huang*

ACM International Conference on Information and Knowledge Management

[paper] [code]

• [CIKM'2023] "How Expressive are Graph Neural Networks in Recommendation?"

X. Cai+, L. Xia, X. Ren, and C. Huang*

ACM International Conference on Information and Knowledge Management

[paper] [code]

• [Recsys'2023] "Multi-Relational Contrastive Learning for Recommendation"

W. Wei+, L. Xia, and C. Huang*

ACM International Conference on Recommender Systems

[paper] [code]

• [ICML'2023] "Spatial-Temporal Graph Learning with Adversarial Contrastive Adaptation"

Q. Zhang+, C. Huang*, L. Xia, Z. Wang, S.M. Yiu, R. Han

International Conference on Machine Learning

[paper] [code] [media]

• [ICML'2023] "When Sparsity Meets Contrastive Models: Less Graph Data Can Bring Better Class-Balanced Representations"

C. Zhang, C. Huang, Y. Tian, Q. Wen, Z. Ouyang, Y. Li, Y. Ye, C. Zhang

International Conference on Machine Learning

[paper] [code]

• [SIGIR'2023] "Graph Transformer for Recommendation"

C. Li+, L. Xia, X. Ren, Y. Ye, Y. Xu and C. Huang*

ACM Conference on Research and Development in Information Retrieval

[paper] [code]

• [SIGIR'2023] "Disentangled Contrastive Collaborative Filtering"

X. Ren+, L. Xia, J. Zhao, D. Yin and C. Huang*

ACM Conference on Research and Development in Information Retrieval

🌟 (Most Influential Papers of SIGIR'2023 - Rank 12th / 165 Accepted Papers) 🌟

[paper] [code] [media]

• [SIGIR'2023] "Graph Masked Autoencoder for Sequential Recommendation"

Y. Ye+, L. Xia and C. Huang*

ACM Conference on Research and Development in Information Retrieval

[paper] [code] [media]

• [ICDE'2023] "Disentangled Graph Social Recommendation"

L. Xia+, Y. Shao, C. Huang*, Y. Xu, H. Xu and J. Pei

IEEE International Conference on Data Engineering

[paper] [code]

• [AAAI'2023] "Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction"

J. Ji+, J. Wang, C. Huang, J. Wu, B. Xu, Z. Wu, J. Zhang and Y. Zheng

The AAAI Conference on Artificial Intelligence

[paper] [code] [media]

In the Year of 2022:

• [KDD'22] "Self-Supervised Hypergraph Transformer for Recommender Systems"

L. Xia+, C. Huang*, and C. Zhang

ACM International Conference on Knowledge Discovery and Data Mining

[paper] [code] [media]

• [KDD'22] "Multi-Behavior Hypergraph-Enhanced Transformer for Sequential Recommendation"

Y. Yang+, C. Huang*, L. Xia, Y. Liang, Y. Yu and C. Li

ACM International Conference on Knowledge Discovery and Data Mining

[paper] [code] [media]

• [KDD'22] "Multiplex Heterogeneous Graph Convolutional Network"

P. Yu, C. Fu, Y. Yu, C. Huang, Z. Zhao and J. Dong

ACM International Conference on Knowledge Discovery and Data Mining

[paper] [code]

• [SIGIR'22] "Hypergraph Contrastive Collaborative Filtering"

L. Xia+, C. Huang*, Y. Xu, J. Zhao, D. Yin and J. Huang

ACM Conference on Research and Development in Information Retrieval

🌟 (Most Influential Papers of SIGIR'2022 - Rank 2nd / 161 Accepted Papers) 🌟

[paper] [code] [media]

• [SIGIR'22] "Knowledge Graph Contrastive Learning for Recommendation"

Y. Yang+, C. Huang*, L. Xia and C. Li

ACM Conference on Research and Development in Information Retrieval

🌟 (Most Influential Papers of SIGIR'2022 - Rank 3rd / 161 Accepted Papers) 🌟

[paper] [code] [media]

• [WSDM'22] "Contrastive Meta Learning with Behavior Multiplicity for Recommendation"

W. Wei+, C. Huang*, L. Xia,  Y. Xu, J. Zhao and D. Yin

ACM International Conference on Web Search and Data Mining

🌟 (Best Paper Nomination & Top-3 Most Cited Paper 3 / 159 Accepted Papers) 🌟

[paper] [code] [media]

• [ICDE'22] "Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Prediction"

Z. Li+, C. Huang*, L. Xia,  Y. Xu and J. Pei

IEEE International Conference on Data Engineering

[paper] [code] [media]

• [ICDE'22] "Scalable Motif Counting for Large-scale Temporal Graphs"

Z. Gao, C. Cheng, Y. Yu, L. Cao, C. Huang and J. Dong

IEEE International Conference on Data Engineering

[paper] [code]

• [TKDE'22] "Multi-Behavior Sequential Recommendation with Temporal Graph Transformer"

L. Xia+, C. Huang*, Y. Xu and J. Pei

IEEE Transactions on Knowledge and Data Engineering

[paper] [code]

• [TNNLS'22] "Multi-Behavior Graph Neural Network for Recommender System"

L. Xia+, C. Huang*, Y. Xu, P. Dai and L. Bo

IEEE Transactions on Neural Networks and Learning Systems

[paper] [code]

• [SIGSPATIAL'22] "When Do Contrastive Learning Signals Help Spatio-Temporal Graph Forecasting?"

X. Liu, Y. Liang, C. Huang, Y. Zheng, B. Hooi and R. Zimmermann

ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

[paper] [code]

• [IJCAI'22] "RecipeRec: A Heterogeneous Graph Learning Model for Recipe Recommendation"

Y. Tian, C. Zhang, Z. Guo, C. Huang, R. Metoyer and N. Chawla

International Joint Conference on Artificial Intelligence

[paper] [code]

In the Year of 2021:

• [IJCAI'21] "Spatial-Temporal Sequential Hypergraph Network for Crime Prediction" 

L. Xia+, C. Huang, Y. Xu, P. Dai, L. Bo, X. Zhang, T. Chen,

International Joint Conference on Artificial Intelligence

[paper] [code]

• [AAAI'21] "Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network" 

X. Zhang+, C Huang*, Y. Xu, L. Xia, P Dai, L. Bo, J. Zhang and Y. Zheng

AAAI Conference on Artificial Intelligence

[paper] [code]

• [AAAI'21] "Knowledge-aware Coupled Graph Neural Network for Social Recommendation" 

C. Huang, H. Xu, Y. Xu, P. Dai, L. Xia, M. Lu, L. Bo, H. Xing, X. Lai, Y. Ye

AAAI Conference on Artificial Intelligence

[paper] [code]

• [AAAI'21] "Graph-Enhanced Multi-Task Learning of Multi-Level Transition Dynamics for Session-based Recommendation" 

C. Huang, J. Chen, L. Xia, Y. Xu, P. Dai, Y. Chen, L. Bo, J. Zhao and J. Huang

AAAI Conference on Artificial Intelligence

[paper] [code]

• [AAAI'21] "Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation"

L. Xia+, C. Huang*, Y. Xu, P. Dai, M. Lu and L. Bo

AAAI Conference on Artificial Intelligence

[paper] [code] [media]

• [SIGIR'21] "Graph Meta Network for Multi-Behavior Recommendation" 

L. Xia+, Y. Xu, C. Huang*, P. Dai and L. Bo

ACM Conference on Research and Development in Information Retrieval

(Top-10 Most Cited Paper among 10/151)

[paper] [code] [media]

• [CIKM'21] "Social Recommendation with Self-Supervised Metagraph Informax Network"

X. Long+, C. Huang, Y. Xu, H. Xu, P. Dai, L. Xia and L. Bo

ACM International Conference on Information and Knowledge Management

[paper] [code] [media]

• [WWW'21] "Motif-Preserving Dynamic Attributed Network Embedding" 

Z. Liu, C. Huang, Y. Yu, J. Dong

ACM The Web Conference

[paper] [code]

• [ICDE'21] "Multi-Behavior Enhanced Recommendation with Cross-Interaction Collaborative Relation Modeling" 

L. Xia+, C. Huang, Y. Xu, P. Dai, M. Lu and L. Bo

IEEE International Conference on Data Engineering

[paper] [code]

• [TOIS'21] "Collaborative Reflection-Augmented Autoencoder Network for Recommender Systems" 

L. Xia+, C. Huang, Y. Xu, H. Xu, X. Li and W. Zhang

ACM Transactions on Information Systems

[paper] [code]

In the Year of 2020:

• [WWW'20] "Hierarchically Structured Transformer Networks for Fine-Grained Spatial Event Prediction" 

C. Huang, X. Wu, C. Zhang and N. Chawla

ACM The Web Conference

[paper] [code]

• [IJCAI'20] "Cross-Interaction Hierarchical Attention Networks for Urban Anomaly Prediction" 

C. Huang, C. Zhang, P. Dai and L. Bo

International Joint Conference on Artificial Intelligence

[paper] [code]

• [SIGIR'20] "Multiplex Behavioral Relation Learning for Recommendation via Memory Augmented Transformer Network" 

C. Huang, L. Xia, Y. Xu, P. Dai and L. Bo

ACM Conference on Research and Development in Information Retrieval

[paper] [code]

• [ICDM'20] "Global Context Enhanced Social Recommendation with Hierarchical Graph Neural Networks" 

H. Xu+, C. Huang, Y. Xu, L. Xia, H. Xing and D. Yin

IEEE International Conference on Data Mining

[paper] [code]

• [CIKM'20] "Fast Attributed Multiplex Heterogeneous Network Embedding"

Z. Liu, C. Huang, Y. Yu, B. Fan and J. Dong

ACM International Conference on Information and Knowledge Management

[paper] [code]

• [CIKM'20] "Dynamic Representation Learning for Large-Scale Attributed Networks" 

Z. Liu, C. Huang, Y. Yu, P. Song, B. Fan and J. Dong

ACM International Conference on Information and Knowledge Management

[paper] [code]

• [CIKM'20] "Spatial-Temporal Convolutional Graph Attention Networks for Citywide Traffic Flow Forecasting" 

X. Zhang+, C. Huang, Y. Xu, and L. Xia

ACM International Conference on Information and Knowledge Management

[paper] [code]

• [AAAI'20] "Few-Shot Knowledge Graph Completion"

C. Zhang, H. Yao, C. Huang, M. Jiang, Z. Li, N. Chawla

AAAI Conference on Artificial Intelligence

[paper] [code]

In the Year of 2019:

• [KDD'19] "Online Purchase Prediction via Multi-Scale Modeling of Behavior Dynamics"

C. Huang, X. Wu, X. Zhang, C. Zhang, J. Zhao, D. Yin and N. Chawla

ACM International Conference on Knowledge Discovery and Data Mining

[paper] [code]

• [WWW'19] "MiST: A Multiview and Multimodal Spatial-Temporal Learning Framework for Citywide Abnormal Event Forecasting"

C. Huang, C. Zhang, J. Zhao, X. Wu, D. Yin and N. Chawla

ACM The Web Conference

🌟 (Best Paper Candidate) 🌟

[paper] [code]

• [CIKM'19] "Deep Dynamic Fusion Network for Traffic Accident Forecasting"

C. Huang, C. Zhang, P. Dai and L. Bo

ACM International Conference on Information and Knowledge Management

[paper] [code]

• [KDD'19] "Heterogeneous Graph Neural Network"

C. Zhang, D. Song, C. Huang, A. Swami and N. Chawla

ACM International Conference on Knowledge Discovery and Data Mining

🌟 (Most Influential Papers of KDD'2019 - Rank 4th /174 Accepted Papers) 🌟

[paper] [code]

• [WSDM'19] "Neural Tensor Factorization for Temporal Interaction Learning"

X. Wu, B. Shi, Y. Dong, C. Huang and N. Chawla

ACM International Conference on Web Search and Data Mining

[paper] [code]