Junjun Guo

513 citations
27 papers · 392 · h-index 11

Impact in

Papers in

    • Pregnancy and preeclampsia studies 4
    • Topic Modeling 5
    • Sentiment Analysis and Opinion Mining 4
    • Text and Document Classification Technologies 3
    • Natural Language Processing Techniques 2
    • Advanced Text Analysis Techniques 2

Junjun Guo

27 papers receiving 386 citations

Peers

Junjun Guo
Comparison fields: 5 of 103
  • Nuclear Energy and Engineering 9
  • Waste Management and Disposal 50
  • Rehabilitation 26
  • Obstetrics and Gynecology 21
  • Pollution 35
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Sima Habibi Iran
Yuyan Cai China
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Junjun Guo relative to Rachna N. Dave India Rachna N. Dave's profile →
Citations per field
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Citations per year

Countries citing papers authored by Junjun Guo

Since Specialization
Citations

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

Fields of papers citing papers by Junjun Guo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 27 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2019132
2 202251
3 202428
4 202327
5 201024
6 202322
7 202216
8 202315
9 202312
10 202111
11 201911
12 20238
13 20208
14 20176
15 20233
16 20233
17 20253
18 20132
19 20252
20 20201

About Junjun Guo

Junjun Guo is a scholar working on Obstetrics and Gynecology, Artificial Intelligence, Polymers and Plastics, Complementary and alternative medicine and Cancer Research, having authored 27 papers that have together received 392 indexed citations. Recurring topics across this work include Topic Modeling (5 papers), Pregnancy and preeclampsia studies (4 papers), Sentiment Analysis and Opinion Mining (4 papers), Text and Document Classification Technologies (3 papers), Natural Language Processing Techniques (2 papers), Advanced Text Analysis Techniques (2 papers), MicroRNA in disease regulation (2 papers) and Wireless Communication Networks Research (1 paper). The work is most often cited by research in Nuclear Energy and Engineering (9 citations), Waste Management and Disposal (50 citations), Rehabilitation (26 citations), Obstetrics and Gynecology (21 citations) and Pollution (35 citations). Junjun Guo has collaborated with scholars based in China, Bangladesh and Egypt. Frequent co-authors include Yangkai Sun, Zhixia He, Benjamin Bernard Uzoejinwa, Qian Wang, Abd El‐Fatah Abomohra, Lili Qian, Lu Liu, Bin Li, Chuan Yuan and Bin Cao. Their work appears in journals such as Frontiers in Immunology, Euphytica, Frontiers in Bioengineering and Biotechnology, Advanced Energy Materials and Nature.

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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