Jeffrey Xu Yu

19.7k total citations · 2 hit papers
418 papers, 11.8k citations indexed

About

Jeffrey Xu Yu is a scholar working on Signal Processing, Computer Networks and Communications and Artificial Intelligence. According to data from OpenAlex, Jeffrey Xu Yu has authored 418 papers receiving a total of 11.8k indexed citations (citations by other indexed papers that have themselves been cited), including 199 papers in Signal Processing, 192 papers in Computer Networks and Communications and 190 papers in Artificial Intelligence. Recurrent topics in Jeffrey Xu Yu's work include Data Management and Algorithms (193 papers), Advanced Database Systems and Queries (116 papers) and Complex Network Analysis Techniques (85 papers). Jeffrey Xu Yu is often cited by papers focused on Data Management and Algorithms (193 papers), Advanced Database Systems and Queries (116 papers) and Complex Network Analysis Techniques (85 papers). Jeffrey Xu Yu collaborates with scholars based in Hong Kong, China and Australia. Jeffrey Xu Yu's co-authors include Lu Qin, Hong Cheng, Xuemin Lin, Yang Zhou, Rong-Hua Li, Lijun Chang, Hongjun Lü, Philip S. Yu, Wei Wang and Lei Zou and has published in prestigious journals such as PLoS ONE, Information Sciences and IEEE Transactions on Knowledge and Data Engineering.

In The Last Decade

Jeffrey Xu Yu

396 papers receiving 11.3k citations

Hit Papers

Graph clustering based on... 2009 2026 2014 2020 2009 2014 100 200 300 400 500

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Jeffrey Xu Yu Hong Kong 54 5.7k 4.8k 4.7k 3.1k 3.0k 418 11.8k
Xuemin Lin Australia 56 5.1k 0.9× 4.9k 1.0× 6.0k 1.3× 2.2k 0.7× 4.2k 1.4× 501 12.7k
Xifeng Yan United States 54 5.8k 1.0× 2.8k 0.6× 2.8k 0.6× 1.5k 0.5× 2.6k 0.9× 179 11.4k
Laks V. S. Lakshmanan Canada 51 3.9k 0.7× 3.4k 0.7× 2.6k 0.6× 3.3k 1.1× 689 0.2× 209 9.1k
Haixun Wang United States 50 6.2k 1.1× 3.1k 0.6× 3.1k 0.7× 831 0.3× 2.1k 0.7× 231 9.6k
Rajeev Rastogi United States 42 6.1k 1.1× 3.8k 0.8× 3.2k 0.7× 711 0.2× 1.6k 0.5× 142 10.0k
Gao Cong Singapore 53 3.5k 0.6× 2.6k 0.5× 4.1k 0.9× 1.5k 0.5× 1.4k 0.5× 297 10.8k
Jennifer Widom United States 70 10.7k 1.9× 14.7k 3.1× 8.5k 1.8× 1.1k 0.4× 1.9k 0.6× 231 21.3k
Ravi Kumar United States 42 3.5k 0.6× 1.5k 0.3× 1.2k 0.3× 1.3k 0.4× 1.2k 0.4× 147 6.6k
Xiaokui Xiao Singapore 53 6.2k 1.1× 1.7k 0.4× 1.7k 0.4× 1.6k 0.5× 993 0.3× 199 9.6k
Lu Qin Australia 42 2.4k 0.4× 2.4k 0.5× 2.2k 0.5× 2.1k 0.7× 1.8k 0.6× 220 5.5k

Countries citing papers authored by Jeffrey Xu Yu

Since Specialization
Citations

This map shows the geographic impact of Jeffrey Xu Yu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jeffrey Xu Yu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jeffrey Xu Yu more than expected).

Fields of papers citing papers by Jeffrey Xu Yu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jeffrey Xu Yu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jeffrey Xu Yu. The network helps show where Jeffrey Xu Yu may publish in the future.

Co-authorship network of co-authors of Jeffrey Xu Yu

This figure shows the co-authorship network connecting the top 25 collaborators of Jeffrey Xu Yu. A scholar is included among the top collaborators of Jeffrey Xu Yu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jeffrey Xu Yu. Jeffrey Xu Yu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Yu, Jeffrey Xu, et al.. (2025). Graph Based K-Nearest Neighbor Search Revisited. ACM Transactions on Database Systems. 50(4). 1–30.
2.
Li, Jia, et al.. (2024). Graph Intelligence with Large Language Models and Prompt Learning. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 6545–6554. 6 indexed citations
3.
Wang, Yiran, Xuemin Lin, Jeffrey Xu Yu, et al.. (2024). Efficient Betweenness Centrality Computation over Large Heterogeneous Information Networks. Proceedings of the VLDB Endowment. 17(11). 3360–3372. 1 indexed citations
4.
Zhong, Ming, et al.. (2024). Evolution Forest Index: Towards Optimal Temporal k -Core Component Search via Time-Topology Isomorphic Computation. Proceedings of the VLDB Endowment. 17(11). 2840–2853.
5.
Zhong, Ming, Yuanyuan Zhu, Tieyun Qian, et al.. (2024). Querying Cohesive Subgraph Regarding Span-Constrained Triangles on Temporal Graphs. 3338–3350.
6.
Dong, Yushun, et al.. (2024). Rethinking Fair Graph Neural Networks from Re-balancing. 1736–1745. 4 indexed citations
7.
Zhao, Kangfei, Jeffrey Xu Yu, Qiyan Li, Hao Zhang, & Yu Rong. (2023). Learned sketch for subgraph counting: a holistic approach. The VLDB Journal. 32(5). 937–962. 7 indexed citations
8.
Altincatal, Arman, et al.. (2021). Linking Ambulance Trip and Emergency Department Surveillance Data on Opioid-Related Overdose, Massachusetts, 2017. Public Health Reports. 136(1_suppl). 47S–53S. 4 indexed citations
9.
Haldar, Nur Al Hasan, Jianxin Li, Mark Reynolds, Timos Sellis, & Jeffrey Xu Yu. (2019). Location prediction in large-scale social networks: an in-depth benchmarking study. The VLDB Journal. 28(5). 623–648. 24 indexed citations
10.
Wang, Chaokun, Changping Wang, Zheng Wang, et al.. (2018). DeepDirect: Learning Directions of Social Ties with Edge-Based Network Embedding. IEEE Transactions on Knowledge and Data Engineering. 31(12). 2277–2291. 32 indexed citations
11.
Li, Rong-Hua, Jianxin Li, Shaojie Qiao, et al.. (2018). Efficient Structural Clustering on Probabilistic Graphs. IEEE Transactions on Knowledge and Data Engineering. 31(10). 1954–1968. 22 indexed citations
12.
Deng, Ke, Yanhua Li, Jia Zeng, et al.. (2018). User Preference Analysis for Most Frequent Peer/Dominator. IEEE Transactions on Knowledge and Data Engineering. 31(7). 1412–1425. 1 indexed citations
13.
Zhao, Kangfei & Jeffrey Xu Yu. (2017). Graph Processing in RDBMSs.. IEEE Data(base) Engineering Bulletin. 40. 6–17. 4 indexed citations
14.
Hu, Sen, Lei Zou, Jeffrey Xu Yu, Haixun Wang, & Dongyan Zhao. (2017). Answering Natural Language Questions by Subgraph Matching over Knowledge Graphs. IEEE Transactions on Knowledge and Data Engineering. 30(5). 824–837. 178 indexed citations
15.
Zhang, Yikai, et al.. (2017). To Meet or Not to Meet: Finding the Shortest Paths in Road Networks. IEEE Transactions on Knowledge and Data Engineering. 30(4). 772–785. 9 indexed citations
16.
Zhao, Peixiang, Jeffrey Xu Yu, & Philip S. Yu. (2007). Graph indexing: tree + delta <= graph. Very Large Data Bases. 938–949. 138 indexed citations
17.
Wang, Shan, Zhaohui Peng, Jun Zhang, et al.. (2006). NUITS: a novel user interface for efficient keyword search over databases. Very Large Data Bases. 1143–1146. 18 indexed citations
18.
Zhang, Yanchun, Katsumi Tanaka, Jeffrey Xu Yu, Shan Wang, & Minglu Li. (2005). Proceedings of the 7th Asia-Pacific web conference on Web Technologies Research and Development. 1 indexed citations
19.
Yu, Jeffrey Xu, et al.. (2004). Selecting Views with Maintenance Cost Constraints: Issues Heuristics and Performance. 4 indexed citations
20.
Fung, Gabriel Pui Cheong, Jeffrey Xu Yu, & Hongjun Lü. (2002). Discriminative Category Matching: Efficient Text Classification for Huge Discriminative Category Matching: Efficient Text Classification for Huge. 187. 2 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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