Meili Tang

1.0k citations
23 papers · 827 indexed · 1 hit paper · h-index 10
Topics
Complex Network Analysis Techniques (6 papers)Privacy-Preserving Technologies in Data (4 papers)Opinion Dynamics and Social Influence (4 papers)

In The Last Decade

Meili Tang

23 papers receiving 800 citations

Hit Papers

Social Network and Tag Sources Based Augmenting Collabora...2015202620182022201550100150200250

Peers

Meili Tang
Comparison fields: 5 of 87
  • Artificial Intelligence 439
  • Information Systems 212
  • Computer Networks and Communications 194
  • Computer Vision and Pattern Recognition 172
  • Statistical and Nonlinear Physics 136
Replace James G. Shanahan with:
James G. Shanahan United States
Ron Bekkerman United States
Parag Singla India
Sujith Ravi United States
Francesco Buccafurri Italy
Zhenming Liu United States
Pinghui Wang China
Richong Zhang China
Nina Mishra United States
Aneesh Sharma United States
Meili Tang relative to James G. Shanahan United States James G. Shanahan's profile →
Citations per field
00.5×
James G. Shanahan · 1×
Citations per year

Countries citing papers authored by Meili Tang

Since Specialization
Citations

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

Fields of papers citing papers by Meili Tang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Meili Tang

This figure shows the co-authorship network connecting the top 25 collaborators of Meili Tang. A scholar is included among the top collaborators of Meili Tang 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 Meili Tang. Meili Tang 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
1 6
2 28
3 1
4 7
5 2
6 23
7 53
8 3
9 9
10 7
11 9
12 76
13 76
14 27
15 131
16
Social Network and Tag Sources Based Augmenting Collaborative Recommender Systembreakdown →
254
17 63
18
Data Mining Based on Privacy Preserving
2
19 3
20 38

About Meili Tang

Meili Tang is a scholar working on Statistical and Nonlinear Physics, Information Systems and Computer Vision and Pattern Recognition, having authored 23 papers that have together received 827 indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (6 papers), Privacy-Preserving Technologies in Data (4 papers) and Opinion Dynamics and Social Influence (4 papers). The work is most often cited by research in Artificial Intelligence (439 citations), Statistical and Nonlinear Physics (136 citations) and Information Systems (212 citations). Meili Tang has collaborated with scholars based in China, Saudi Arabia and South Korea. Frequent co-authors include Tinghuai Ma, Mznah Al‐Rodhaan, Abdullah Al‐Dhelaan, Yuan Tian, Yuan Tian, Abdullah Al‐Dhelaan, Jie Cao, Jie Cao, Xu Yang and Wei Tian. Their work appears in journals such as IEEE Access, International Journal of Environmental Research and Public Health and Neurocomputing.

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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