Keke Huang (黄柯柯)
Research Fellow
School of Computing, National University of Singapore
13 Computing Drive, Singapore 117417
Email: kkhuang@nus.edu.sg
Short Bio
I am currently a Research Fellow in the School of Computing (SoC) at National University of Singapore (NUS). I received my Ph.D. degree from Nanyang Technological University (NTU), under the supervision of Prof. Xiaokui Xiao and Prof. Aixin Sun. Before that, I got my B.Eng. Degree from Huazhong University of Science and Technology (HUST). I am a long-term visitor of the AI Group of Prof. Pietro Liò at University of Cambridge (2023).
Research Interests
Graph Algorithm Design & Analytics
Approximation Algorithms in Social Networks
Graph Neural Networks
Selected Publications (* indicates equal contributions)
How Universal Polynomial Bases Enhance Spectral Graph Neural Networks: Heterophily, Over-smoothing, and Over-squashing. [pdf]
Keke Huang, Yu Guang Wang, Ming Li, and Pietro Liò.
Proceedings of the International Conference on Machine Learning (ICML), to appear, 2024.
Scalable Continuous-time Diffusion Framework for Network Inference and Influence Estimation. [pdf][Code]
Keke Huang, Ruize Gao, Bogdan Cautis, and Xiaokui Xiao.
Proceedings of the ACM Web Conference (TheWebConf), 2024. [Oral Presentation]
Keke Huang, Wencai Cao, Hoang Ta, Xiaokui Xiao, and Pietro Liò.
Proceedings of the ACM Web Conference (TheWebConf), 2024
Keke Huang, Jing Tang, Juncheng Liu, Renchi Yang, and Xiaokui Xiao.
Proceedings of the ACM Web Conference (TheWebConf), 2023.
Efficient and Effective Edge-wise Graph Representation Learning. [pdf]
Hewen Wang, Renchi Yang, Keke Huang, and Xiaokui Xiao.
Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD), 2023.
Scalable and Effective Bipartite Network Embedding. [pdf]
Renchi Yang, Jieming Shi, Keke Huang, and Xiaokui Xiao.
Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD), 2022.
Optimal Streaming Algorithms for Multi-Armed Bandits. [pdf]
Tianyuan Jin, Keke Huang, Jing Tang, and Xiaokui Xiao.
Proceedings of the International Conference on Machine Learning (ICML), 2021.
Almost Optimal Anytime Algorithm for Batched Multi-Armed Bandits. [pdf]
Tianyuan Jin, Jing Tang, Pan Xu, Keke Huang, Xiaokui Xiao, and Quanquan Gu.
Proceedings of the International Conference on Machine Learning (ICML), 2021.
Unconstrained Submodular Maximization with Modular Costs: Tight Approximation and Application to Profit Maximization. [pdf]
Tianyuan Jin, Yu Yang, Renchi Yang, Jieming Shi, Keke Huang, and Xiaokui Xiao.
Proceedings of the VLDB Endowment (PVLDB), 2021.
Effective and Scalable Clustering on Massive Attributed Graphs. [arXiv]
Renchi Yang, jieming Shi, Yin Yang, Keke Huang, Shiqi Zhang, Xiaokui Xiao
Proceedings of The Web Conference (TheWebConf) 2021.
Keke Huang*, Jing Tang*, Kai Han, Xiaokui Xiao, Wei Chen, Aixin Sun, Xueyan Tang, and Andrew Lim.
The International Journal on Very Large Data Bases (VLDBJ), 2020.
Keke Huang, Jing Tang, Xiaokui Xiao, Aixin Sun, and Andrew Lim.
Proceedings of the IEEE International Conference on Data Engineering (ICDE), 2020.
Best Bang for the Buck: Cost-Effective Seed Selection for Online Social Networks. [pdf]
Kai Han, Yuntian He, Keke Huang, Xiaokui Xiao, Shaojie Tang, Jingxin Xu, Liusheng Huang.
IEEE Transactions on Knowledge and Data Engineering (TKDE), 2019.
Jing Tang*, Keke Huang*, Xiaokui Xiao, Laks V.S. Lakshmanan, Xueyan Tang, Aixin Sun, and Andrew Lim.
Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD), 2019.
Efficient Algorithms for Adaptive Influence Maximization. [pdf]
Kai Han*, Keke Huang*, Xiaokui Xiao*, Jing Tang, Aixin Sun, Xueyan Tang.
Proceedings of the VLDB Endowment (PVLDB), 11(9):1029-1040, 2018.
Revisiting the Stop-and-Stare Algorithms for Influence Maximization. [pdf]
Keke Huang, Sibo Wang, Glenn Bevilacqua, Xiaokui Xiao, and Laks V.S. Lakshmanan.
Proceedings of the VLDB Endowment (PVLDB), 10(9):913-924, 2017.
Invited Talks
Spectral Graph Neural Network: Polynomial Approximation and Optimization by Prof. Lio at University of Cambridge.
Spectral Graph Neural Network: Polynomial Approximation and Optimization by Prof. Laks at The University of British Columbia.
Node-wise Diffusion for Scalable Graph Learning by Prof. Xuanhua Shi at Huazhong University of Science and Technology.
Academic Services
Conference Program Committee Member：
ACM Web Conference (WWW): 2022, 2023, 2024
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD): 2022, 2023, 2024
Conference on Neural Information Processing Systems (NeurIPS): 2023
ACM International Conference on Web Search and Data Mining (WSDM): 2023
ACM International Conference on Information and Knowledge Management (CIKM): 2023, 2024
Learning on Graphs Conference (LoG): 2022, 2023, 2024
Journal Reviewer of VLDBJ, TKDE, Neurocomputing, Journal of Global Optimization, ACM Computing Surveys, Applied Network Science, Mathematics.
Teaching Assistant
Database System
Java and the Internet
Computer Organization and Architecture