Position Openings

I am looking for PhD students, Research Assistants and Research Fellows. Please send me your CV if you are interested.

Research Interests

• Big Data Management and Analytics

• Social Network and Graph Analysis

• Machine Learning

• Blockchains

Publications
(“#” indicates co-first authors, and “*” indicates the corresponding author.)

1. [WWW '24] Xiaolong Chen, Yifan Song, and Jing Tang*.
   Link Recommendation to Augment Influence Diffusion with Provable Guarantees.
   Proceedings of the ACM Web Conference (WWW), pages xxx--xxx, 2024.

2. [ICLR '24] Jing Xiong, Zixuan Li, Chuanyang Zheng, Zhijiang Guo, Yichun Yin, Enze Xie, Zhicheng Yang, Qingxing Cao, Haiming Wang, Xiongwei Han, Jing Tang, Chengming Li, and Xiaodan Liang.
   DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context Learning.
   Proceedings of the 12th International Conference on Learning Representations (ICLR), pages xxx--xxx, 2024.

3. [CoNLL '23] Yiwei Wang, Bryan Hooi, Fei Wang, Yujun Cai, Yuxuan Liang, Wenxuan Zhou, Jing Tang, Manjuan Duan, and Muhao Chen.
   How Fragile is Relation Extraction under Entity Replacements?
   Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL), pages 414–423, 2023.

4. [KDD '23] Shiqi Zhang, Renchi Yang, Jing Tang, Xiaokui Xiao, and Bo Tang.
   Efficient Approximation Algorithms for Spanning Centrality.
   Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pages 3386–3395, 2023. 

5. [IJCAI '23] Yuhan Chen#, Yihong Luo#, Jing Tang*, Liang Yang, Siya Qiu, Chuan Wang*, and Xiaochun Cao.
   LSGNN: Towards General Graph Neural Network in Node Classification by Local Similarity.
   Proceedings of the 32nd International Joint Conference on Artificial Intelligence (IJCAI), pages 3550–3558, 2023.

6. [WWW '23] He Huang, Kai Han, Shuang Cui, and Jing Tang.
   Randomized Pricing with Deferred Acceptance for Revenue Maximization with Submodular Objectives.
   Proceedings of the ACM Web Conference (WWW), pages 3530–3540, 2023.
   [PDF]

7. [WWW '23] Shuang Cui, Kai Han, Jing Tang, and He Huang.
   Constrained Subset Selection from Data Streams for Profit Maximization.
   Proceedings of the ACM Web Conference (WWW), pages 1822–1831, 2023.
   [PDF]

8. [WWW '23] Keke Huang, Jing Tang, Juncheng Liu, Renchi Yang, and Xiaokui Xiao.
   Node-wise Diffusion for Scalable Graph Learning.
   Proceedings of the ACM Web Conference (WWW), pages 1723–1733, 2023.
   [PDF]

9. [AAAI '23] Shuang Cui, Kai Han, Jing Tang, He Huang, Xueying Li, and Zhiyu Li.
   Practical Parallel Algorithms for Submodular Maximization subject to a Knapsack Constraint with Nearly Optimal Adaptivity.
   Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI), pages 7261–7269, 2023.
   [PDF]

10. [SIGMOD '23] Renchi Yang and Jing Tang*.
    Efficient Estimation of Pairwise Effective Resistance.
    Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD), pages 1(1):16:1–16:27, 2023.
    [PDF]

11. [TKDE '23] Yuqing Zhu, Jing Tang*, Xueyan Tang, Sibo Wang, and Andrew Lim.
    2-hop+ Sampling: Efficient and Effective Influence Estimation.
    IEEE Transactions on Knowledge and Data Engineering (TKDE), 35(2):1088–1103, 2023.
    [PDF]

12. [SIGMETRICS '23 & POMACS '22] Shuang Cui, Kai Han, Jing Tang, He Huang, Xueying Li, and Zhiyu Li.
    Streaming Algorithms for Constrained Submodular Maximization.
    Proceedings of the ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS), pages 65–66, 2023.
    Proceedings of the ACM on Measurement and Analysis of Computing Systems (POMACS), 6(3):54:1–54:32, 2022.
    [PDF]

13. [NeurIPS '22] Qing Xiu, Kai Han, Jing Tang, Shuang Cui, and He Huang.
    Chromatic Correlation Clustering, Revisited.
    Advances in Neural Information Processing Systems (NeurIPS), pages 26147–26159, 2022.
    [PDF]

14. [KDD '22] Qianhao Cong, Jing Tang*, Kai Han, Yuming Huang, Lei Chen, and Yeow Meng Chee.
    Noisy Interactive Graph Search.
    Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pages 231–240, 2022.
    [PDF]

15. [TODS '22] Qintian Guo, Sibo Wang, Zhewei Wei, Wenqing Lin, and Jing Tang.
    Influence Maximization Revisited: Efficient Sampling with Bound Tightened.
    ACM Transactions on Database Systems (TODS), 47(3):12:1–12:45, 2022.
    [PDF]

16. [VLDBJ '22] Yuqing Zhu, Jing Tang*, and Xueyan Tang.
    Optimal Price Profile for Influential Nodes in Online Social Networks.
    The VLDB Journal (VLDBJ), 31(4):779–795, 2022.
    [PDF]

17. [ICDE '22] Jing Tang*, Yuqing Zhu, Xueyan Tang, and Kai Han.
    Distributed Influence Maximization for Large-Scale Online Social Networks.
    Proceedings of the IEEE International Conference on Data Engineering (ICDE), pages 81–95, 2022.
    [PDF]

18. [ICDE '22] Qianhao Cong, Jing Tang*, Yuming Huang, Lei Chen, and Yeow Meng Chee.
    Cost-Effective Algorithms for Average-Case Interactive Graph Search.
    Proceedings of the IEEE International Conference on Data Engineering (ICDE), pages 1152–1165, 2022.
    [PDF] [arXiv]

19. [PVLDB '22] Yuqing Zhu, Jing Tang*, Xueyan Tang, and Lei Chen.
    Analysis of Influence Contribution in Social Advertising.
    Proceedings of the VLDB Endowment (PVLDB), 15(2):348–360, 2022.
    [PDF]

20. [NeurIPS '21] Yining Ma, Jingwen Li, Zhiguang Cao, Wen Song, Le Zhang, Zhenghua Chen, and Jing Tang.
    Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer.
    Advances in Neural Information Processing Systems (NeurIPS), pages 11096–11107, 2021.
    [PDF]

21. [ICML '21] Tianyuan Jin, Jing Tang, Pan Xu, Keke Huang, Xiaokui Xiao, and Quanquan Gu.
    Almost Optimal Anytime Algorithm for Batched Multi-Armed Bandits.
    Proceedings of the International Conference on Machine Learning (ICML), pages 5065–5073, 2021.
    [PDF]

22. [ICML '21] Tianyuan Jin, Keke Huang, Jing Tang, and Xiaokui Xiao.
    Optimal Streaming Algorithms for Multi-Armed Bandits.
    Proceedings of the International Conference on Machine Learning (ICML), pages 5045–5054, 2021.
    [PDF]

23. [ICML '21] Shuang Cui, Kai Han, Tianshuai Zhu, Jing Tang, Benwei Wu, He Huang.
    Randomized Algorithms for Submodular Function Maximization with a k-System Constraint.
    Proceedings of the International Conference on Machine Learning (ICML), pages 2222–2232, 2021.
    [PDF] [arXiv]

24. [SIGMOD '21] Kai Han, Benwei Wu, Jing Tang, Shuang Cui, Cigdem Aslay, and Laks V.S. Lakshmanan.
    Efficient and Effective Algorithms for Revenue Maximization in Social Advertising.
    Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD), pages 671–684, 2021.
    [PDF]

25. [SIGMOD '21] Yuming Huang#, Jing Tang#*, Qianhao Cong, Andrew Lim, and Jianliang Xu.
    Do the Rich Get Richer? Fairness Analysis for Blockchain Incentives.
    Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD), pages 790–803, 2021.
    [PDF] [arXiv]

26. [SIGMETRICS '21 & POMACS '21] Jing Tang*, Xueyan Tang, Andrew Lim, Kai Han, Chongshou Li, and Junsong Yuan.
    Revisiting Modified Greedy Algorithm for Monotone Submodular Maximization with a Knapsack Constraint.
    Proceedings of the ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS), pages 63–64, 2021.
    Proceedings of the ACM on Measurement and Analysis of Computing Systems (POMACS), 5(1):08:1–08:22, 2021.
    [PDF] [Abstract] [arXiv] [Acceptance Rate: 12.1% (38 out of 315)]

27. [ACM MM '20] Xinke Li, Chongshou Li, Zekun Tong, Andrew Lim, Junsong Yuan, Yuwei Wu, Jing Tang, and Raymond Huang.
    Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor Scene.
    Proceedings of the ACM International Conference on Multimedia (ACM MM), pages 238–246, 2020. (Oral)
    [PDF] [arXiv] [Acceptance Rate (Oral): 8.9% (151 out of 1698)]

28. [PVLDB '20] Yuqing Zhu, Jing Tang*, and Xueyan Tang.
    Pricing Influential Nodes in Online Social Networks.
    Proceedings of the VLDB Endowment (PVLDB), 13(10):1614–1627, 2020.
    [PDF]

29. [ICDCS '20] Jun Zhao, Jing Tang, Zengxiang Li, Huaxiong Wang, Kwok-Yan Lam, and Kaiping Xue.
    An Analysis of Blockchain Consistency in Asynchronous Networks: Deriving a Neat Bound.
    Proceedings of the 40th IEEE International Conference on Distributed Computing Systems (ICDCS), pages 179–189, 2020.
    [PDF] [arXiv] [Acceptance Rate: 18.0% (105 out of 584)]

30. [VLDBJ '20] Keke Huang#, Jing Tang#*, Kai Han, Xiaokui Xiao, Wei Chen, Aixin Sun, Xueyan Tang, and Andrew Lim.
    Efficient Approximation Algorithms for Adaptive Influence Maximization.
    The VLDB Journal (VLDBJ), 29(6):1385–1406, 2020.
    [PDF] [arXiv] 

31. [ICDE '20] Keke Huang, Jing Tang*, Xiaokui Xiao, Aixin Sun, and Andrew Lim.
    Efficient Approximation Algorithms for Adaptive Target Profit Maximization.
    Proceedings of the IEEE International Conference on Data Engineering (ICDE), pages 649–660, 2020.
    [PDF] [arXiv]

32. [JSAC '19] Jing Tang* and Richard T. B. Ma.
    Regulating Monopolistic ISPs without Neutrality.
    IEEE Journal on Selected Areas in Communications (JSAC), 37(7):1666–1680, 2019.
    [PDF]

33. [SIGMOD '19] Jing Tang#*, Keke Huang#, Xiaokui Xiao, Laks V.S. Lakshmanan, Xueyan Tang, Aixin Sun, and Andrew Lim.
    Efficient Approximation Algorithms for Adaptive Seed Minimization.
    Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD), pages 1096–1113, 2019.
    [PDF] [arXiv]

34. [PVLDB '19] Kai Han, Fei Gui, Xiaokui Xiao, Jing Tang, Yuntian He, Zongmai Cao, and He Huang.
    Efficient and Effective Algorithms for Clustering Uncertain Graphs.
    Proceedings of the VLDB Endowment (PVLDB), 12(6):667–680, 2019.
    [PDF]

35. [PVLDB '18] Kai Han, Keke Huang, Xiaokui Xiao, Jing Tang, Aixin Sun, and Xueyan Tang.
    Efficient Algorithms for Adaptive Influence Maximization.
    Proceedings of the VLDB Endowment (PVLDB), 11(9):1029–1040, 2018.
    [PDF]

36. [SIGMOD '18] Jing Tang*, Xueyan Tang, Xiaokui Xiao, and Junsong Yuan.
    Online Processing Algorithms for Influence Maximization.
    Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD), pages 991–1005, 2018.
    [PDF] [Code]

37. [INFOCOM '18] Jing Tang*, Xueyan Tang, and Junsong Yuan.
    Towards Profit Maximization for Online Social Network Providers.
    Proceedings of the IEEE International Conference on Computer Communications (INFOCOM), pages 1178–1186, 2018.
    [PDF] [arXiv] [Acceptance Rate: 19.2% (309 out of 1606), Best-in-Session Presentation Award]

38. [TKDE '18] Jing Tang*, Xueyan Tang, and Junsong Yuan.
    Profit Maximization for Viral Marketing in Online Social Networks: Algorithms and Analysis.
    IEEE Transactions on Knowledge and Data Engineering (TKDE), 30(6):1095–1108, 2018.
    [PDF] [Code]

39. [TMM '18] Jing Tang*, Xueyan Tang, and Junsong Yuan.
    Traffic-Optimized Data Placement for Social Media.
    IEEE Transactions on Multimedia (TMM), 20(4):1008–1023, 2018.
    [PDF]

40. [SNAM '18] Jing Tang*, Xueyan Tang, and Junsong Yuan.
    An Efficient and Effective Hop-Based Approach for Influence Maximization in Social Networks.
    Social Network Analysis and Mining journal (SNAM), 8(1):10:1–10:19, 2018.
    [PDF] [Code]

41. [ASONAM '17] Jing Tang*, Xueyan Tang, and Junsong Yuan.
    Influence Maximization Meets Efficiency and Effectiveness: A Hop-Based Approach.
    Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pages 64–71, 2017.
    [PDF] [arXiv] [Code] [Acceptance Rate: 17.2% (33 out of 192)]

42. [ICNP '16] Jing Tang*, Xueyan Tang, and Junsong Yuan.
    Profit Maximization for Viral Marketing in Online Social Networks.
    Proceedings of the 24th IEEE International Conference on Network Protocols (ICNP), pages 1–10, 2016.
    [PDF] [Code] [Acceptance Rate: 20.0% (46 out of 229)]

43. [ICDCS '15] Jing Tang*, Xueyan Tang, and Junsong Yuan.
    Optimizing Inter-Server Communication for Online Social Networks.
    Proceedings of the 35th IEEE International Conference on Distributed Computing Systems (ICDCS), pages 215–224, 2015.
    [PDF] [Acceptance Rate: 12.9% (70 out of 543)]

44. [ICNP '14] Jing Tang* and Richard T. B. Ma.
    Regulating Monopolistic ISPs without Neutrality.
    Proceedings of the 22nd IEEE International Conference on Network Protocols (ICNP), pages 374–384, 2014.
    [PDF] [Acceptance Rate: 20.0% (32 out of 160), Best Paper Award (1 out of 32)]

Honors and Awards

• Finalist for the Singapore NRF Fellowship for Artificial Intelligence, Class of 2019

• Best-in-Session Presentation Award: The IEEE International Conference on Computer Communications (INFOCOM), 2018

• Best Paper Award: The 22nd IEEE International Conference on Network Protocols (ICNP), 2014

• NTU Research Scholarship, 2013-2017

• Outstanding Graduate Scholarship: University of Science and Technology of China (USTC), 2012

Press Releases

• Tech it forward. The Strait Times, 1 July 2018.

Work Experiences

• Assistant Professor at The Hong Kong University of Science and Technology (Guangzhou), 2021 to date

• Assistant Professor at The Hong Kong University of Science and Technology, 2021 to date

• Research Assistant Professor at National University of Singapore, 2018–2021

• Research Fellow at Nanyang Technological University, 2017–2018

• Technical Manager at Massif Studio, Singapore, 2016–2017

• Technical Manager at iBeautyGuru, Singapore, 2015–2016

• Research Assistant at National University of Singapore, 2012–2013