Tan Yang

859 citations
45 papers · 605 · h-index 13

Impact in

Papers in

Tan Yang

37 papers receiving 595 citations

Peers

Tan Yang
Comparison fields: 5 of 93
  • Polymers and Plastics 154
  • Statistical and Nonlinear Physics 99
  • Artificial Intelligence 137
  • Computer Networks and Communications 91
  • Information Systems 79
Replace Yezhou Wu with:
Yezhou Wu China
Zhi Yu China
Anh Tuan Luu Singapore
Fenghua Li China
Osvaldo N. Oliveira Brazil
Jiwei Qin China
Jidong Zhang China
Dinghan Shen United States
Tan Yang relative to Yezhou Wu China Yezhou Wu's profile →
Citations per field
00.5×4.6×
Yezhou Wu · 1×
Citations per year

Countries citing papers authored by Tan Yang

Since Specialization
Citations

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

Fields of papers citing papers by Tan Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 45 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2021156
2 202058
3 201556
4 202138
5 201938
6 201532
7 202029
8 201728
9 202025
10 202124
11 201915
12 202215
13 201615
14 201611
15 20209
16 20117
17 20205
18 20125
19 20174
20 20134

About Tan Yang

Tan Yang is a scholar working on Computer Networks and Communications, Information Systems, Artificial Intelligence, Electrical and Electronic Engineering and Statistical and Nonlinear Physics, having authored 45 papers that have together received 605 indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (7 papers), Network Security and Intrusion Detection (6 papers), Anomaly Detection Techniques and Applications (4 papers), Cloud Computing and Resource Management (4 papers), Image and Video Quality Assessment (4 papers), Caching and Content Delivery (4 papers), Peer-to-Peer Network Technologies (4 papers) and Sentiment Analysis and Opinion Mining (4 papers). The work is most often cited by research in Polymers and Plastics (154 citations), Statistical and Nonlinear Physics (99 citations), Artificial Intelligence (137 citations), Computer Networks and Communications (91 citations) and Information Systems (79 citations). Tan Yang has collaborated with scholars based in China, Switzerland and United States. Frequent co-authors include Yuehui Jin, Jun‐Long Niu, Xinlei Zou, Chunyang Jia, Qiyao Wang, Shiduan Cheng, Xiaolong Weng, Yi Wang, Longjiang Deng and Zhen Lin. Their work appears in journals such as Physica A Statistical Mechanics and its Applications, Knowledge-Based Systems, IEEE Access, Applied Materials Today and Applied Intelligence.

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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