Lianhua Chi

1.2k citations
57 papers · 669 indexed · h-index 16
Topics
Advanced Graph Neural Networks (8 papers)Time Series Analysis and Forecasting (7 papers)Network Security and Intrusion Detection (6 papers)

In The Last Decade

Lianhua Chi

52 papers receiving 647 citations

Peers

Lianhua Chi
Comparison fields: 5 of 109
  • Artificial Intelligence 373
  • Computer Vision and Pattern Recognition 136
  • Computer Networks and Communications 133
  • Signal Processing 96
  • Information Systems 85
Replace Wenchao Yu with:
Wenchao Yu United States
Hanrui Wu China
Mohd Hanafi Ahmad Hijazi Malaysia
Jinwen Liang China
Mahua Bhattacharya India
Ghufran Ahmad Khan China
Vani Rajasekar India
Damien Magoni France
Rodrigo Olivares Chile
Lianhua Chi relative to Wenchao Yu United States Wenchao Yu's profile →
Citations per field
00.5×1.5×
Wenchao Yu · 1×
Citations per year

Countries citing papers authored by Lianhua Chi

Since Specialization
Citations

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

Fields of papers citing papers by Lianhua Chi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lianhua Chi

This figure shows the co-authorship network connecting the top 25 collaborators of Lianhua Chi. A scholar is included among the top collaborators of Lianhua Chi based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Lianhua Chi. Lianhua Chi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
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5 24
6 44
7 5
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10 16
11 1
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Full-blind delegating private quantum computation
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15
End-to-end Network for Twitter Geolocation Prediction and Hashing
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16 16
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Geolocation Prediction in Twitter Using Location Indicative Words and Textual Features
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18 14
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About Lianhua Chi

Lianhua Chi is a scholar working on Health Informatics, Signal Processing and Artificial Intelligence, having authored 57 papers that have together received 669 indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (8 papers), Time Series Analysis and Forecasting (7 papers) and Network Security and Intrusion Detection (6 papers). The work is most often cited by research in Artificial Intelligence (373 citations), Signal Processing (96 citations) and Computer Vision and Pattern Recognition (136 citations). Lianhua Chi has collaborated with scholars based in Australia, China and United States. Frequent co-authors include Xingquan Zhu, Yi‐Ping Phoebe Chen, Shirui Pan, Yu Zheng, Ming Jin, Bin Li, Wei Xiang, Yuan-Fang Li, Huan Yee Koh and Haishuai Wang. Their work appears in journals such as Food Chemistry, Expert Systems with Applications and IEEE Access.

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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