J. Y. Shi

5.5k total citations
17 papers, 124 citations indexed

About

J. Y. Shi is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Computer Vision and Pattern Recognition. According to data from OpenAlex, J. Y. Shi has authored 17 papers receiving a total of 124 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 10 papers in Statistical and Nonlinear Physics and 9 papers in Computer Vision and Pattern Recognition. Recurrent topics in J. Y. Shi's work include Advanced Graph Neural Networks (12 papers), Complex Network Analysis Techniques (10 papers) and Graph Theory and Algorithms (9 papers). J. Y. Shi is often cited by papers focused on Advanced Graph Neural Networks (12 papers), Complex Network Analysis Techniques (10 papers) and Graph Theory and Algorithms (9 papers). J. Y. Shi collaborates with scholars based in United States, China and Australia. J. Y. Shi's co-authors include José M. F. Moura, Jian Du, Lavender Yao Jiang, Soummya Kar, Wenhui Yuan and Xintao Li and has published in prestigious journals such as IEEE Transactions on Signal Processing, IEEE Signal Processing Magazine and Archives of Microbiology.

In The Last Decade

J. Y. Shi

12 papers receiving 120 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
J. Y. Shi United States 6 68 30 26 19 16 17 124
Ryo Karakida Japan 8 68 1.0× 37 1.2× 38 1.5× 8 0.4× 4 0.3× 22 159
Philipp Petersen Austria 6 29 0.4× 30 1.0× 25 1.0× 21 1.1× 8 0.5× 16 96
Gintare Karolina Dziugaite United Kingdom 5 88 1.3× 74 2.5× 10 0.4× 10 0.5× 4 0.3× 12 165
Dimitris C. Dracopoulos United Kingdom 6 55 0.8× 14 0.5× 20 0.8× 6 0.3× 25 1.6× 14 117
Rana Ali Amjad Germany 7 105 1.5× 54 1.8× 9 0.3× 4 0.2× 10 0.6× 13 175
Jure Sokolić United Kingdom 5 82 1.2× 62 2.1× 9 0.3× 23 1.2× 3 0.2× 9 141
Andrey Voynov Russia 7 22 0.3× 97 3.2× 7 0.3× 15 0.8× 3 0.2× 14 200
Wasim Huleihel Israel 7 58 0.9× 9 0.3× 9 0.3× 18 0.9× 103 6.4× 27 203
Luke Metz United States 5 104 1.5× 139 4.6× 20 0.8× 5 0.3× 3 0.2× 12 223

Countries citing papers authored by J. Y. Shi

Since Specialization
Citations

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

Fields of papers citing papers by J. Y. Shi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by J. Y. Shi. 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 J. Y. Shi. The network helps show where J. Y. Shi may publish in the future.

Co-authorship network of co-authors of J. Y. Shi

This figure shows the co-authorship network connecting the top 25 collaborators of J. Y. Shi. A scholar is included among the top collaborators of J. Y. Shi 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 J. Y. Shi. J. Y. Shi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

17 of 17 papers shown
2.
Shi, J. Y., et al.. (2025). Applications of endophytic fungi in plant disease control. Archives of Microbiology. 207(5). 117–117. 1 indexed citations
3.
Shi, J. Y. & José M. F. Moura. (2024). Sampling in the Graph Signal Processing Companion Model. 1–5.
4.
Shi, J. Y. & José M. F. Moura. (2024). Graph Signal Processing: The 2D Companion Model. 9806–9810.
5.
Shi, J. Y., et al.. (2024). Graph Convolutional Neural Networks In The Companion Model. 7045–7049. 2 indexed citations
6.
Shi, J. Y. & José M. F. Moura. (2023). Extending DSP to Graph Signal Processing: The Companion Approach. 335–339.
7.
Shi, J. Y., et al.. (2023). Graph Classification via Simple Graph Based Features. abs/1710.10370. 583–587.
8.
Shi, J. Y. & José M. F. Moura. (2022). From DSP to GSP: Sampling in Both Domains. 2 indexed citations
9.
Shi, J. Y. & José M. F. Moura. (2022). Graph Signal Processing: Dualizing GSP Sampling in the Vertex and Spectral Domains. IEEE Transactions on Signal Processing. 70. 2883–2898. 13 indexed citations
10.
Shi, J. Y., et al.. (2021). Using Sparse Spectral Shifts in Graph CNNs. 2021 55th Asilomar Conference on Signals, Systems, and Computers. abs 1710 10370. 1536–1540.
11.
Shi, J. Y., et al.. (2020). Graph Signal Processing and Deep Learning: Convolution, Pooling, and Topology. IEEE Signal Processing Magazine. 37(6). 139–149. 43 indexed citations
12.
Shi, J. Y., et al.. (2020). A Dual Approach to Graph CNNs. 12. 1467–1471. 2 indexed citations
13.
Shi, J. Y., et al.. (2019). Pooling in Graph Convolutional Neural Networks. 462–466. 16 indexed citations
14.
Shi, J. Y. & José M. F. Moura. (2019). Topics in Graph Signal Processing: Convolution and Modulation. 457–461. 10 indexed citations
15.
Shi, J. Y., et al.. (2018). Classification with Vertex-Based Graph Convolutional Neural Networks. 2018 52nd Asilomar Conference on Signals, Systems, and Computers. abs 1511 7289. 752–756. 4 indexed citations
16.
Du, Jian, J. Y. Shi, Soummya Kar, & José M. F. Moura. (2018). ON GRAPH CONVOLUTION FOR GRAPH CNNS. 1–5. 13 indexed citations
17.
Shi, J. Y.. (2005). Rocket Engine Nozzle Side Load Transient Analysis Methodology- a Practical Approach. 46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference. 17 indexed citations

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.

Explore authors with similar magnitude of impact

Rankless by CCL
2026