Chengyao Chen

405 citations
14 papers · 228 · h-index 11

Impact in

Papers in

    • Advanced Text Analysis Techniques 5
    • Advanced Graph Neural Networks 4
    • Sentiment Analysis and Opinion Mining 4
    • Topic Modeling 3
    • Advanced Clustering Algorithms Research 1
    • Complex Network Analysis Techniques 11
    • Opinion Dynamics and Social Influence 7

Chengyao Chen

14 papers receiving 219 citations

Peers

Chengyao Chen
Comparison fields: 5 of 34
  • Statistical and Nonlinear Physics 137
  • Artificial Intelligence 150
  • Computational Mathematics 2
  • Information Systems 52
  • Transportation 15
Replace Peng Bao with:
Peng Bao China
Ralitsa Angelova Germany
René Schult Germany
Liudmila Prokhorenkova Russia
Xiaobao Wang China
Norbert Blenn Netherlands
Timothy La Fond United States
Jerry Scripps United States
Nazanin Alipourfard United States
Chengyao Chen relative to Peng Bao China Peng Bao's profile →
Citations per field
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Citations per year

Countries citing papers authored by Chengyao Chen

Since Specialization
Citations

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

Fields of papers citing papers by Chengyao Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 16 scholars most cited alongside Chengyao Chen, 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 Chengyao Chen Line = papers co-authored together Chengyao Chen links everyone, so they are left out of the graph.

All Works

14 of 14 papers shown
#Work
1 201853
2 201744
3 201817
4 201916
5 201814
6 202013
7 201413
8 201812
9 201812
10 202011
11 201610
12
Content-based influence modeling for opinion behavior prediction
20166
13 20176
14 20171

About Chengyao Chen

Chengyao Chen is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics, Sociology and Political Science, Information Systems and Cognitive Neuroscience, having authored 14 papers that have together received 228 indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (11 papers), Opinion Dynamics and Social Influence (7 papers), Advanced Text Analysis Techniques (5 papers), Advanced Graph Neural Networks (4 papers), Sentiment Analysis and Opinion Mining (4 papers), Topic Modeling (3 papers), Advanced Clustering Algorithms Research (1 paper) and Computational and Text Analysis Methods (1 paper). The work is most often cited by research in Statistical and Nonlinear Physics (137 citations), Artificial Intelligence (150 citations), Computational Mathematics (2 citations), Information Systems (52 citations) and Transportation (15 citations). Chengyao Chen has collaborated with scholars based in Hong Kong, China and United States. Frequent co-authors include Wenjie Li, Zhitao Wang, Yuexian Hou, Dehong Gao, Xu Sun, Ji Zhang, Chao He, Pengfei Liu, Cane Wing-ki Leung and Furu Wei. Their work appears in journals such as ACM Transactions on Knowledge Discovery from Data, IEEE Intelligent Systems, Transactions of the Association for Computational Linguistics, IEEE Transactions on Knowledge and Data Engineering and IEEE Transactions on Affective Computing.

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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