Xiaochun Yang

2.4k citations
175 papers · 1.7k · h-index 20

Impact in

Papers in

Xiaochun Yang

153 papers receiving 1.7k citations

Peers

Xiaochun Yang
Comparison fields: 5 of 100
  • Signal Processing 441
  • Artificial Intelligence 964
  • Information Systems 421
  • Computer Networks and Communications 426
  • Management Science and Operations Research 222
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Ni Lao United States
Rui Mao China
Markus Weimer United States
Da Yan United States
Hillol Kargupta United States
Guisheng Yin China
Weining Qian China
Zied Elouedi Tunisia
Giorgio Terracina Italy
Kenji Yamanishi Japan
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Citations per field
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Citations per year

Countries citing papers authored by Xiaochun Yang

Since Specialization
Citations

This map shows the geographic impact of Xiaochun Yang'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 Xiaochun Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiaochun Yang more than expected).

Fields of papers citing papers by Xiaochun Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Xiaochun Yang. 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 Xiaochun Yang. The network helps show where Xiaochun Yang may publish in the future.

Co-authors

The 25 scholars most cited alongside Xiaochun Yang, 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 Xiaochun Yang Line = papers co-authored together Xiaochun Yang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 175 papers — load more, or switch the sort, to bring in the rest.

#Work
1
VGRAM: improving performance of approximate queries on string collections using variable-length grams
2007148
2 201889
3 201784
4 200878
5 201066
6 202058
7 201955
8 200452
9 200940
10 200832
11 200932
12 201728
13 201127
14 201726
15 201826
16 200925
17 201521
18 201320
19 201320
20 201220

About Xiaochun Yang

Xiaochun Yang is a scholar working on Artificial Intelligence, Computer Networks and Communications, Signal Processing, Information Systems and Computer Vision and Pattern Recognition, having authored 175 papers that have together received 1.7k indexed citations. Recurring topics across this work include Data Management and Algorithms (44 papers), Algorithms and Data Compression (26 papers), Privacy-Preserving Technologies in Data (24 papers), Advanced Database Systems and Queries (22 papers), Cryptography and Data Security (17 papers), Network Packet Processing and Optimization (15 papers), Advanced Image and Video Retrieval Techniques (14 papers) and Advanced Graph Neural Networks (12 papers). The work is most often cited by research in Signal Processing (441 citations), Artificial Intelligence (964 citations), Information Systems (421 citations), Computer Networks and Communications (426 citations) and Management Science and Operations Research (222 citations). Xiaochun Yang has collaborated with scholars based in China, Australia and United States. Frequent co-authors include Bin Wang, Chen Li, Jianxin Li, Guoren Wang, Chengfei Liu, Ningning Cui, Ge Yu, Bin Wang, Bin Wang and Bin Wang. Their work appears in journals such as IEEE Transactions on Knowledge and Data Engineering, World Wide Web, Lecture notes in computer science, IEEE Access and IEEE Transactions on Automation Science and 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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