Jeffrey Xu Yu

19.7k citations
418 papers · 11.8k indexed · 2 hit papers · h-index 54

Jeffrey Xu Yu

396 papers receiving 11.3k citations

Hit Papers

Querying k-truss community in large and dynamic graphs3272009202620142020100200300400500

Peers

Jeffrey Xu Yu
Comparison fields: 5 of 147
  • Signal Processing 4.7k
  • Statistical and Nonlinear Physics 3.1k
  • Computer Networks and Communications 4.8k
  • Artificial Intelligence 5.7k
  • Computer Vision and Pattern Recognition 3.0k
Replace Xuemin Lin with:
Xuemin Lin Australia
Laks V. S. Lakshmanan Canada
Haixun Wang United States
Xiaokui Xiao Singapore
Rajeev Rastogi United States
Jennifer Widom United States
Gao Cong Singapore
Xifeng Yan United States
Nick Koudas Canada
Reynold Cheng Hong Kong
Jeffrey Xu Yu relative to Xuemin Lin Australia Xuemin Lin's profile →
Citations per field
00.5×1.5×
Xuemin Lin · 1×
Citations per year

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

The 25 scholars most cited alongside Jeffrey Xu Yu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jeffrey Xu Yu Line = papers co-authored together Jeffrey Xu Yu links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20250
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4 20241
5 20240
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7 20249
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9 20240
10 20232
11 202310
12 20214
13 201819
14 20184
15 20181
16 201824
17 2017178
18 20179
19 20088
20
Indexable PLA for efficient similarity search
200774

About Jeffrey Xu Yu

Jeffrey Xu Yu is a scholar working on Signal Processing, Computer Networks and Communications and Statistical and Nonlinear Physics, having authored 418 papers that have together received 11.8k indexed citations. Recurring topics across this work include Data Management and Algorithms (193 papers), Advanced Database Systems and Queries (116 papers), Complex Network Analysis Techniques (85 papers), Graph Theory and Algorithms (83 papers), Advanced Graph Neural Networks (81 papers), Data Mining Algorithms and Applications (43 papers), Semantic Web and Ontologies (37 papers) and Caching and Content Delivery (32 papers). The work is most often cited by research in Signal Processing (4.7k citations), Statistical and Nonlinear Physics (3.1k citations) and Computer Networks and Communications (4.8k citations). Jeffrey Xu Yu has collaborated with scholars based in Hong Kong, China and Australia. Frequent 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. Their work appears in journals such as PLoS ONE, Information Sciences and IEEE Transactions on Knowledge and Data Engineering.

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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