Xinyu Que

503 citations
17 papers · 287 · h-index 7

Impact in

Papers in

Xinyu Que

16 papers receiving 270 citations

Peers

Xinyu Que
Comparison fields: 5 of 63
  • Hardware and Architecture 55
  • Computer Networks and Communications 175
  • Information Systems 120
  • Statistical and Nonlinear Physics 51
  • Computer Vision and Pattern Recognition 54
Replace Kamer Kaya with:
Kamer Kaya United States
Michał Podstawski Switzerland
Abdou Youssef United States
Fangzhe Chang United States
Mihai Capotă Netherlands
Paulo Sérgio Almeida Portugal
Maurizio Drocco Italy
Ke Zhai China
Ville Tuulos Finland
Jilong Xue China
Xinyu Que relative to Kamer Kaya United States Kamer Kaya's profile →
Citations per field
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Kamer Kaya · 1×
Citations per year

Countries citing papers authored by Xinyu Que

Since Specialization
Citations

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

Fields of papers citing papers by Xinyu Que

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

17 of 17 papers shown
#Work
1 201591
2 201176
3 201636
4 201316
5 201416
6 201615
7 201512
8 20205
9 20174
10 20153
11 20243
12 20123
13 20163
14 20112
15 20251
16 20101
17 20180

About Xinyu Que

Xinyu Que is a scholar working on Computer Networks and Communications, Hardware and Architecture, Information Systems, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 17 papers that have together received 287 indexed citations. Recurring topics across this work include Parallel Computing and Optimization Techniques (10 papers), Advanced Data Storage Technologies (7 papers), Cloud Computing and Resource Management (6 papers), Graph Theory and Algorithms (5 papers), Interconnection Networks and Systems (3 papers), Advanced Graph Neural Networks (3 papers), Distributed and Parallel Computing Systems (2 papers) and Complex Network Analysis Techniques (2 papers). The work is most often cited by research in Hardware and Architecture (55 citations), Computer Networks and Communications (175 citations), Information Systems (120 citations), Statistical and Nonlinear Physics (51 citations) and Computer Vision and Pattern Recognition (54 citations). Xinyu Que has collaborated with scholars based in United States, Switzerland and China. Frequent co-authors include Fabrizio Petrini, Fabio Checconi, Weikuan Yu, John A. Gunnels, Yandong Wang, Cong Xu, Yan‐Dong Wang, Xing Liu, Yogish Sabharwal and Venkatesan T. Chakaravarthy. Their work appears in journals such as Computer, IEEE Transactions on Parallel and Distributed Systems, Journal of Chemical Information and Modeling, IEEE Transactions on Computers and Computer Science - Research and Development.

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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