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陈亮

Liang Chen

@Guangzhou, China.

About me

Dr. Liang Chen is currently an Associate Professor at School of Computer Science and Engineering in Sun Yat-Sen University, Guang Zhou, China. He received a PhD and a Bachelor’s degree from Advanced Computing and System Laboratory (CCNT), College of Compter Science &Technology at Zhejiang University, China, respectively in 2015 and 2009. His research area includes Graph Learning, Trustworthy AI, Large Language Model, and Recommendation System. (Google Scholar)

Position Opening! Looking for Students with Research Passion! (Research Intern, Master, PhD.)

What’s new

  • [TCSS-24] 2024/04/04, our work on “FairAGG: Towards Fair Graph Neural Networks via Fair Aggregation” has been accepted.
  • [TCSS-24] 2024/3/11, our work on “A Review-level Sentiment Information Enhanced Multi-task Learning Approach for Explainable Recommendation” has been accepted.
  • [WWW-24] 2024/1/23, our work on “Fair Graph Representation Learning via Sensitive Attribute Disentanglement” has been accepted.
  • [ICLR-24] 2024/1/16, our work on “A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks” has been accepted.

Latest Selected Publications

  • [TCSS-24] Yuchang Zhu, Jintang Li, Liang Chen*, Zibin Zheng, Mingdong Tang. “A Review-level Sentiment Information Enhanced Multi-task Learning Approach for Explainable Recommendation”, IEEE Transactions on Computational Social Systems, April 2024, accepted.
  • [TCSS-24] Fenfang Xie, Yuansheng Wang, Kun Xu, Liang Chen*, Zibin Zheng, Mingdong Tang. “A Review-level Sentiment Information Enhanced Multi-task Learning Approach for Explainable Recommendation”, IEEE Transactions on Computational Social Systems, March 2024, accepted.
  • [WWW-24] Yuchang Zhu, Jintang Li, Zibin Zheng, Liang Chen*. “Fair Graph Representation Learning via Sensitive Attribute Disentanglement”, In Proceedings of International World Wide Web Conference, May 13th-17th, Singapore, accepted.
  • [ICLR-24] Jintang Li, Huizhe Zhang, Ruofan Wu, Zulun Zhu, Baokun Wang, Changhua Meng, Zibin Zheng, Liang Chen*. “A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks”, In Proceedings of International Conference on Learning Representations, May 7th-11th, 2024, Vienna, Austria, accepted.
  • [WSDM-23] Yuchang Zhu, Jintang Li, Liang Chen*, Zibin Zheng. “The Devil is in the Data: Learning Fair Graph Neural Networks via Partial Knowledge Distillation”, the 17th ACM International Conference Web Search and Data Mining, Mar, 2024, pp. 1012-1021.
  • [WSDM-23] Wangbin Sun, Jintang Li, Liang Chen*, Yatao Bian, Bingzhe wu, and Zibin Zheng. “Rethinking and Simplifying Bootstrapped Graph Latents”, the 17th ACM International Conference Web Search and Data Mining, Mar, 2024, pp. 665-673.
  • [EMNLP-23] Wang HaoTian, Zhen Zhang, Mengting Hu, Qichao Wang, Liang Chen, Yatao Bian, and Bingzhe wu, “RECAL: Sample-Relation Guided Confidence Calibration over Tabular Data”, the 2023 Conference on Empirical Methods in Natural Language Processing, Dec, 2023, pp. 7246-7257.
  • [ICDM-23] Xinzhou Jin, Jintang Li, Yuanzhen Xie, Liang Chen*, Beibei Kong, Lei Cheng, Bo Hu, Zang Li, and Zibin Zheng, “Enhancing Graph Collaborative Filtering via Neighborhood Structure Embedding”, the 23rd IEEE International Conference on Data Mining, Dec, 2023, pp. 190-199.
  • [CIKM-23] Jie Liao, Jintang Li, Liang Chen*, Bingzhe Wu, Yatao Bian and Zibin Zheng, “SAILOR: Structural Augmentation Based Tail Node Representation Learning”, the 32nd ACM International Conference on Information and Knowledge Management, October 2023, pp. 1389–1399.
  • [CIKM-23] Jintang Li, Jie Liao, Ruofan Wu, Liang Chen*, Zibin Zheng, Jiawang Dan, Changhua Meng and Weiqiang Wang, “GUARD: Graph Universal Adversarial Defense”, the 32nd ACM International Conference on Information and Knowledge Management, October 2023, pp. 1198–1207.
  • [KDD-23] Jintang Li, Ruofan Wu, Wangbin Sun, Liang Chen*, Sheng Tian, Liang Zhu, Changhua Meng, Zibin Zheng, Weiqiang Wang, “What’s Behind the Mask: Understanding Masked Graph Modeling for Graph Autoencoders”, the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, August 2023, pp. 1268–1279.
  • [ACM-TURC-23] Qichao Wang, Huan Ma, Wentao Wei, Hangyu Li, Changqing Zhang, Peilin Zhao, Binwen Zhao, Bo Hu, Shu Zhang, Bingzhe Wu and Liang Chen*, “Attention Paper: How Generative AI Reshapes Digital Shadow Industry?”, In Proceedings of the ACM Turing Award Celebration Conference - China 2023 (ACM TURC ‘23). Association for Computing Machinery, New York, NY, USA, pp. 143–144.
  • [IJCAI-23] Sheng Tian, Jihai Dong, Jintang Li, Wenlong Zhao, Xiaolong Xu, Baokun Wang, Bowen Song, Changhua Meng, Tianyi Zhang, Liang Chen, “SAD: Semi-Supervised Anomaly Detection on Dynamic Graphs”, the 32nd International Joint Conference on Artificial Intelligence, August 2023, pp. 2306-2314.
  • [SIGIR-23] Wei Yuan, Quoc Viet Hung Nguyen, Tieke He, Liang Chen and Hongzhi Yin, “Manipulating Federated Recommender Systems: Poisoning with Synthetic Users and Its Countermeasures”, the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, July 2023, pp. 1690-1699
  • [TKDE-23] Jintang Li, Tao Xie, Liang Chen*, Fenfang Xie, Xiangnan He, Zibin Zheng, “Adversarial Attack on Large Scale Graph”, IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 1, pp. 82-95, 1 Jan. 2023, doi: 10.1109/TKDE.2021.3078755.
  • [ICDE-23] Yang Liu, Liang Chen*, Xiangnan He, Jiaying Peng, Zibin Zheng, Jie Tang, “Modelling High-Order Social Relations for Item Recommendation”, ICDE 2023 TKDE Poster session, Anaheim, California, USA, April 3 – 7, 2023, accepted.
  • [TKDE-23] Tingting Liang, Congying Xia, Haoran Xu, Ziqiang Zhao, Yuyu Yin, Liang Chen, and Philip S. Yu, “Modeling Reviews for Few-shot Recommendation via Enhanced Prototypical Network”, IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 9, pp. 9407-9420, 1 Sept. 2023, doi: 10.1109/TKDE.2023.3239169.
  • [AAAI-23] Jintang Li, Zhouxin Yu, Zulun Zhu, Liang Chen*, Qi Yu, Zibin Zheng, Sheng Tian, Ruofan Wu, Changhua Meng, “Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks”, 37th AAAI Conference on Artificial Intelligence, Vol. 37, Issue 7, pp.8588-8596.
  • [ICDE-23] Zishan Gu, Ke Zhang, Guangji Bai, Liang Chen, Liang Zhao, Carl Yang, “Dynamic Activation of Clents and Parameters for Federated Learning over Heterogeneous Graphs”, 39th IEEE International Conference on Data Engineering, Anaheim, California, USA, April 2023, pp.1597-1610.
  • [TKDE-22] Jintang Li, Jiaying Peng, Liang Chen*, Zibin Zheng, Tingting Liang, Qing Ling, “Spectral Adversarial Training for Robust Graph Neural Network”, IEEE Transactions on Knowledge and Data Engineering, 2022, doi: 10.1109/TKDE.2022.3222207.
  • [FedGraph-22] Zishan Gu, Ke Zhang, Guangji Bai, Liang Chen, Liang Zhao, Carl Yang, “Dynamic Activation of Clients and Parameters for Federated Learning over Heterogeneous Graphs”, The 1st International Workshop on Federated Learning with Graph Data at ACM CIKM, Atlanta, Georgia, United States, Oct 21, 2022, accepted.
  • [TKDE-22] Yang Liu, Liang Chen*, Xiangnan He, Jiaying Peng, Zibin Zheng, Jie Tang, “Modelling High-Order Social Relations for Item Recommendation”, in IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 9, pp. 4385-4397, 1 Sept. 2022, doi: 10.1109/TKDE.2020.3039463.
  • [KBS-22] Zhouxin Yu, Jintang Li, Liang Chen* and Zibin Zheng, “Unifying Multi-associations through Hypergraph for Bundle Recommendation”, Knowledge-based Systems, August 2022, vol 255, pp. 109755.
  • [KDD-22] Bingzhe Wu, Yatao Bian, Hengtong Zhang, Jintang Li, Junchi Yu, Liang Chen, Chaochao Chen, Junzhou Huang, “Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection”, Washington DC, USA, August 14-18, 2022, pp. 4838-4839.
  • [IJCAI-22] Zulun Zhu, Jiaying Peng, Jintang Li, Liang Chen*, Qi Yu, Siqiang Luo, “Spiking Graph Convolutional Networks”, 31th International Joint Conference on Artificial Intelligence, Main Track, Vienna, Australia, July 23-29, 2022, pp. 2434-2240.
  • [KBS-22] Liang Chen, Tao Xie, Jintang Li, and Zibin Zheng, “Graph Enhanced Neural Interaction Model”, Knowledge-based Systems, March 2022, vol 246, pp. 108616.
  • [IJCAI-21] Liang Chen, Jintang Li, Qibiao Peng, Yang Liu, Zibin Zheng, Carl Yang, “Understanding Structural Vulnerability in Graph Convolutional Networks”, 30th International Joint Conference on Artificial Intelligence, Main Track, Montreal-themed Virtual Reality, Canada, 2021, pp. 2249-2255.
  • [ICSE-21] Jintang Li, Kun Xu, Liang Chen*, Zibin Zheng and Xiao Liu, “GraphGallery: A Platform for Fast Benchmarking and Easy Development of Graph Neural Networks Based Intelligent Software”, 43rd International Conference on Software Engineering, demo track, Virtual, 2021, pp. 13-16, doi: 10.1109/ICSE-Companion52605.2021.00024.
  • [ICDE-21] Tao Xie, Yangjun Xu, Liang Chen*, Yang Liu, and Zibin Zheng, “Sequential Recommendation on Dynamic Heterogeneous Information Network”, IEEE 37th International Conference on Data Engineering, Poster, Chania, Greece, 2021, pp. 2105-2110.
  • [TOIT-21] Liang Chen, Jiaying Peng, Yang Liu, Jintang Li, Fenfang Xie, Zibin Zheng*, “Phishing Scams Detection in Ethereum Transaction Network”, ACM Transactions on Internet Technology, February 2021, vol 21, issue 1, pp. 1-16. [Data]