Yongyi Mao

116 papers receiving 2.0k citations

Hit Papers

Aspect-Level Sentiment Analysis Via Convolution over Depe...2019202620212023201950100150200250

Peers

Yongyi Mao
Comparison fields: 5 of 128
  • Artificial Intelligence 1.0k
  • Computer Networks and Communications 694
  • Electrical and Electronic Engineering 637
  • Molecular Biology 350
  • Computer Vision and Pattern Recognition 193
Replace Hyojoon Kim with:
Hyojoon Kim United States
Patric R. J. Östergård Finland
Guoliang Chen China
Ming‐Yang Kao United States
Junwei Zhang China
Shankar Vembu United States
Stjepan Picek Netherlands
Stefan Arnborg Sweden
Mike Loukides United States
Cliff Young United States
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Citations per field
00.5×3.4×
Hyojoon Kim · 1×
Citations per year

Countries citing papers authored by Yongyi Mao

Since Specialization
Citations

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

Fields of papers citing papers by Yongyi Mao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yongyi Mao

This figure shows the co-authorship network connecting the top 25 collaborators of Yongyi Mao. A scholar is included among the top collaborators of Yongyi Mao 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 Yongyi Mao. Yongyi Mao 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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Aspect-Level Sentiment Analysis Via Convolution over Dependency Treebreakdown →
276
10
The APVA-TURBO Approach To Question Answering in Knowledge Base
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11 28
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On hyper-parameter estimation in empirical Bayes: a revisit of the MacKay algorithm
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On the representation and embedding of knowledge bases beyond binary relations
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Improved DV-Hop algorithm based on error weighting and distance correction
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Improved algorithm of wireless sensor network node localization
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Taylor-series expansion location algorithm based on RBF neural network
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Study on the Ambiguity and Non-Solution of 4-Station TDOA Space Location Systems
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Reduced-Complexity Decoding of Raptor Codes over Fading Channels
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About Yongyi Mao

Yongyi Mao is a scholar working on Artificial Intelligence, Computational Mathematics and Computer Networks and Communications, having authored 127 papers that have together received 2.1k indexed citations. Recurring topics across this work include Topic Modeling (42 papers), Natural Language Processing Techniques (21 papers) and Error Correcting Code Techniques (20 papers). The work is most often cited by research in Artificial Intelligence (1.0k citations), Computer Networks and Communications (694 citations) and Electrical and Electronic Engineering (637 citations). Yongyi Mao has collaborated with scholars based in Canada, China and United Kingdom. Frequent co-authors include Richong Zhang, Jeff Castura, Samuel Mensah, Amir H. Banihashemi, Kai Sun, Xudong Liu, Hongyu Guo, Eric J. Foss, Tim Hamilton and Andrew Emili. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Information Theory 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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