Keke Huang (黄柯柯)
Keke Huang (黄柯柯)
Postdoctoral Researcher
Data Science Institute, Department of Computer Science
The University of British Columbia
DMM Lab, 2366 Main Mall, Vancouver, BC, Canada V6T 1Z4
Email: hkk992@gmail.com kk.huang@ubc.ca
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Selected Publications (* indicates equal contributions)
Selected Publications (* indicates equal contributions)
ThriftLLM: On Cost-Effective Selection of Large Language Models for Classification Queries. [pdf]
Keke Huang, Yimin Shi, Dujian Ding, Yifei Li, Yang Fei, Laks Lakshmanan, and Xiaokui Xiao.
Proceedings of the VLDB Endowment (PVLDB), 2025.
How Universal Polynomial Bases Enhance Spectral Graph Neural Networks: Heterophily, Over-smoothing, and Over-squashing. [pdf][code][video]
Keke Huang, Yu Guang Wang, Ming Li, and Pietro Liò.
Proceedings of the International Conference on Machine Learning (ICML), 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.
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