Vince Lyzinski

26 papers receiving 467 citations

Peers

Vince Lyzinski
Comparison fields: 5 of 75
  • Statistical and Nonlinear Physics 192
  • Statistics and Probability 64
  • Artificial Intelligence 237
  • Computer Vision and Pattern Recognition 129
  • Signal Processing 43
Replace Donniell E. Fishkind with:
Donniell E. Fishkind United States
Minh Tang United States
Aiyou Chen United States
Amin Vahdat United States
Bin Qin China
Anup Rao United States
Luh Yen Belgium
Paul Cuff United States
Avanti Athreya United States
Lei Yu China
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Citations per year

Countries citing papers authored by Vince Lyzinski

Since Specialization
Citations

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

Fields of papers citing papers by Vince Lyzinski

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 28 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201580
2 201567
3
Statistical inference on random dot product graphs: a survey
201847
4 201645
5 201541
6 201933
7 201733
8 201733
9 201326
10 201515
11 20129
12 20169
13 20187
14 20157
15 20156
16 20165
17 20204
18
A limit theorem for scaled eigenvectors of random dot product graphs
20133
19
A central limit theorem for an omnibus embedding of random dot product graphs
20173
20 20222

About Vince Lyzinski

Vince Lyzinski is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics, Computer Vision and Pattern Recognition, Mathematical Physics and Statistics and Probability, having authored 28 papers that have together received 484 indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (12 papers), Advanced Graph Neural Networks (10 papers), Graph Theory and Algorithms (8 papers), Stochastic processes and statistical mechanics (5 papers), Markov Chains and Monte Carlo Methods (3 papers), Random Matrices and Applications (3 papers), Topological and Geometric Data Analysis (2 papers) and Graph theory and applications (2 papers). The work is most often cited by research in Statistical and Nonlinear Physics (192 citations), Statistics and Probability (64 citations), Artificial Intelligence (237 citations), Computer Vision and Pattern Recognition (129 citations) and Signal Processing (43 citations). Vince Lyzinski has collaborated with scholars based in United States and Uruguay. Frequent co-authors include Carey E. Priebe, Minh Tang, Donniell E. Fishkind, Avanti Athreya, Joshua T Vogelstein, Daniel L. Sussman, Youngser Park, Keith Levin, Guillermo Sapiro and R. Jacob Vogelstein. Their work appears in journals such as Journal of Computational and Graphical Statistics, Journal of Theoretical Probability, Bernoulli, The Annals of Applied Statistics and Applied Network Science.

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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