Xiaolin Gui

2.8k citations
143 papers · 1.9k · 1 hit paper · h-index 20

Impact in

Papers in

Xiaolin Gui

135 papers receiving 1.8k citations

Xiaolin Gui's Hit Papers

Deep Convolution Neural Networks for Twitter Sentiment Analysis 2018 · 317 citations
3170+2+5Years since publication100200300

Peers

Xiaolin Gui
Comparison fields: 5 of 98
  • Computer Science Applications 264
  • Artificial Intelligence 1.1k
  • Information Systems 693
  • Computer Networks and Communications 608
  • Transportation 110
Replace Linke Guo with:
Linke Guo United States
An Liu China
Jinbo Xiong China
Hui Zhu China
Zhenjie Zhang China
Zaobo He United States
Lingjuan Lyu China
Mudhakar Srivatsa United States
Xiaolin Gui relative to Linke Guo United States Linke Guo's profile →
Citations per field
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Citations per year

Countries citing papers authored by Xiaolin Gui

Since Specialization
Citations

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

Fields of papers citing papers by Xiaolin Gui

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 143 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Deep Convolution Neural Networks for Twitter Sentiment Analysis
Hit paper breakdown →
2018317
2 2017243
3 2020103
4 201565
5 201450
6 201647
7 202246
8 201943
9 201742
10 201642
11 201337
12 201837
13 201735
14 202031
15 202228
16 201526
17 201125
18 201525
19 202023
20 200922

About Xiaolin Gui

Xiaolin Gui is a scholar working on Artificial Intelligence, Computer Networks and Communications, Information Systems, Sociology and Political Science and Computer Vision and Pattern Recognition, having authored 143 papers that have together received 1.9k indexed citations. Recurring topics across this work include Privacy-Preserving Technologies in Data (36 papers), Cryptography and Data Security (32 papers), Mobile Crowdsensing and Crowdsourcing (18 papers), Cloud Data Security Solutions (18 papers), Human Mobility and Location-Based Analysis (17 papers), Caching and Content Delivery (13 papers), Privacy, Security, and Data Protection (12 papers) and IoT and Edge/Fog Computing (10 papers). The work is most often cited by research in Computer Science Applications (264 citations), Artificial Intelligence (1.1k citations), Information Systems (693 citations), Computer Networks and Communications (608 citations) and Transportation (110 citations). Xiaolin Gui has collaborated with scholars based in China, Hong Kong and United States. Frequent co-authors include Xuejun Zhang, Jian An, Xin He, Xiaoyong Li, Wendong Zhang, Defu Li, Huadóng Ma, Cong Wang, Yiliang Han and Yifeng Zheng. Their work appears in journals such as IEEE Access, IEEE Internet of Things Journal, Journal of Network and Computer Applications, China Communications and Computer Networks.

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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