Yijun Liu

670 citations
30 papers · 471 indexed · h-index 12

Yijun Liu

29 papers receiving 451 citations

Peers

Yijun Liu
Comparison fields: 5 of 91
  • Statistical and Nonlinear Physics 151
  • Communication 63
  • Experimental and Cognitive Psychology 59
  • Cognitive Neuroscience 83
  • Sociology and Political Science 183
Replace Alkım Almila Akdağ Salah with:
Alkım Almila Akdağ Salah Netherlands
Matheus Araújo United States
Souneil Park Spain
Jeremy Boy United States
Thomas Lansdall-Welfare United Kingdom
Javier Lerch United States
Matthew T. McMahon United States
Norbou Buchler United States
Amir Ghasemian United States
Francesco Barbieri Spain
Yijun Liu relative to Alkım Almila Akdağ Salah Netherlands Alkım Almila Akdağ Salah's profile →
Citations per field
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Alkım Almila Akdağ Salah · 1×
Citations per year

Countries citing papers authored by Yijun Liu

Since Specialization
Citations

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

Fields of papers citing papers by Yijun Liu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20241
2 20240
3 20241
4 20238
5 20236
6 20221
7 20225
8 202218
9 20225
10 20225
11 20219
12 20204
13 201916
14 20185
15 20186
16
Prediction of Negative Conversion Days of Childhood Nephrotic Syndrome Based on the Improved Backpropagation Neural Network with Momentum
20151
17 201330
18 2013124
19 201215
20 20096

About Yijun Liu

Yijun Liu is a scholar working on Statistical and Nonlinear Physics, Communication and Human-Computer Interaction, having authored 30 papers that have together received 471 indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (15 papers), Opinion Dynamics and Social Influence (11 papers), Misinformation and Its Impacts (5 papers), Social Media and Politics (4 papers), Sentiment Analysis and Opinion Mining (3 papers), Gaze Tracking and Assistive Technology (2 papers), Bioinformatics and Genomic Networks (2 papers) and Technology Adoption and User Behaviour (2 papers). The work is most often cited by research in Statistical and Nonlinear Physics (151 citations), Communication (63 citations) and Experimental and Cognitive Psychology (59 citations). Yijun Liu has collaborated with scholars based in China, South Korea and United Kingdom. Frequent co-authors include Ying Lian, Xuefan Dong, Yu-Fei Wang, Ping Cheng, Jie Tian, Kai Yuan, Tao Dong, Karen M. von Deneen, Limei Zhao and Wei Qin. Their work appears in journals such as SHILAP Revista de lepidopterología, PLoS ONE and Expert Systems with Applications.

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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