Bin Shi

675 citations
54 papers · 390 · h-index 10

Impact in

Papers in

    • Anomaly Detection Techniques and Applications 7
    • Imbalanced Data Classification Techniques 6
    • Topic Modeling 5
    • Advanced Graph Neural Networks 4
    • Cloud Computing and Resource Management 10

Bin Shi

46 papers receiving 376 citations

Peers

Bin Shi
Comparison fields: 5 of 101
  • Rehabilitation 34
  • Computer Networks and Communications 72
  • Pharmacology 26
  • Information Systems 64
  • Artificial Intelligence 87
Replace Thomas Keller with:
Thomas Keller Germany
Jaehak Yu South Korea
Shiva Shankar Reddy India
Zan Zhou China
Binfeng Wang China
Guanlin Wu China
Milan Marković Serbia
Hafsa Maryam Pakistan
Bin Shi relative to Thomas Keller Germany Thomas Keller's profile →
Citations per field
00.5×9.1×
Thomas Keller · 1×
Citations per year

Countries citing papers authored by Bin Shi

Since Specialization
Citations

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

Fields of papers citing papers by Bin Shi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 54 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202077
2 201964
3 201621
4 202320
5 201919
6 201516
7 202216
8 202313
9 202212
10 202311
11 20219
12 20159
13 20149
14 20197
15 20167
16 20186
17 20146
18 20236
19 20245
20 20235

About Bin Shi

Bin Shi is a scholar working on Artificial Intelligence, Information Systems, Computer Networks and Communications, Signal Processing and Building and Construction, having authored 54 papers that have together received 390 indexed citations. Recurring topics across this work include Cloud Computing and Resource Management (10 papers), Anomaly Detection Techniques and Applications (7 papers), Imbalanced Data Classification Techniques (6 papers), Advanced Data Storage Technologies (6 papers), Traffic Prediction and Management Techniques (5 papers), Topic Modeling (5 papers), IoT and Edge/Fog Computing (4 papers) and Advanced Graph Neural Networks (4 papers). The work is most often cited by research in Rehabilitation (34 citations), Computer Networks and Communications (72 citations), Pharmacology (26 citations), Information Systems (64 citations) and Artificial Intelligence (87 citations). Bin Shi has collaborated with scholars based in China, United States and Singapore. Frequent co-authors include Xijing He, Feng Zhang, Qinghua Zheng, Bo Dong, Hua Wei, Haiying Shen, Xiaofeng Chen, Xue Zhang, Zan Yue and Jing Wang. Their work appears in journals such as Engineering, Expert Systems with Applications, Future Internet, Knowledge-Based Systems and IEEE Access.

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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