Joan Lu

2.6k citations
185 papers · 1.6k · h-index 20

Impact in

Papers in

Joan Lu

174 papers receiving 1.4k citations

Peers

Joan Lu
Comparison fields: 5 of 146
  • Signal Processing 209
  • Computer Networks and Communications 422
  • Information Systems 413
  • Artificial Intelligence 333
  • Health Informatics 12
Replace Xiaofei He with:
Xiaofei He China
Shanshan Tu China
G. Ramkumar India
Muhammad Waqas Pakistan
Hao Zhong China
Liang Qiao China
Xu Yang China
Shruti Patil India
Jian Wu United States
Nenad Stojanović Germany
Joan Lu relative to Xiaofei He China Xiaofei He's profile →
Citations per field
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Xiaofei He · 1×
Citations per year

Countries citing papers authored by Joan Lu

Since Specialization
Citations

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

Fields of papers citing papers by Joan Lu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 185 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2008120
2 201598
3 200997
4 201973
5 200969
6 201864
7 200847
8 202143
9 202240
10 201438
11 200934
12 201730
13 201530
14 202026
15 202125
16 200125
17 201625
18
Proceedings of The 2005 International Conference on Internet Computing, ICOMP 2005
200724
19 201722
20 201319

About Joan Lu

Joan Lu is a scholar working on Information Systems, Artificial Intelligence, Computer Networks and Communications, Mechanical Engineering and Mechanics of Materials, having authored 185 papers that have together received 1.6k indexed citations. Recurring topics across this work include Advanced Database Systems and Queries (17 papers), High Temperature Alloys and Creep (17 papers), Semantic Web and Ontologies (16 papers), Fatigue and fracture mechanics (16 papers), Data Management and Algorithms (15 papers), Metallurgy and Material Forming (13 papers), Data Mining Algorithms and Applications (11 papers) and Cloud Computing and Resource Management (9 papers). The work is most often cited by research in Signal Processing (209 citations), Computer Networks and Communications (422 citations), Information Systems (413 citations), Artificial Intelligence (333 citations) and Health Informatics (12 citations). Joan Lu has collaborated with scholars based in United Kingdom, China and New Zealand. Frequent co-authors include Qiang Xu, Kamin Whitehouse, Hui Li, Guijun Xian, Fadi Thabtah, Lizhen Wang, Marianne Cherrington, Gang Zhou, Chieh‐Yih Wan and Mark Yarvis. Their work appears in journals such as Materials at High Temperatures, PLoS ONE, Sensors, Information Sciences and Scientific Reports.

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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