Ryan G. James

827 citations
21 papers · 464 · h-index 13

Impact in

Papers in

Ryan G. James

19 papers receiving 455 citations

Peers

Ryan G. James
Comparison fields: 5 of 96
  • Statistical and Nonlinear Physics 162
  • Cognitive Neuroscience 137
  • Artificial Intelligence 106
  • Computational Theory and Mathematics 48
  • Economics and Econometrics 74
Replace Siyang Leng with:
Siyang Leng China
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Countries citing papers authored by Ryan G. James

Since Specialization
Citations

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

Fields of papers citing papers by Ryan G. James

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ryan G. James. 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 Ryan G. James. The network helps show where Ryan G. James may publish in the future.

Co-authors

The 22 scholars most cited alongside Ryan G. James, 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 Ryan G. James Line = papers co-authored together Ryan G. James links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 21 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201693
2 201454
3 201449
4 200140
5 201738
6 201835
7 201623
8 198821
9 201818
10 201417
11
Synchronization and Control in Intrinsic and Designed Computation: An\n Information-Theoretic Analysis of Competing Models of Stochastic Computation
201016
12 201416
13 201115
14 201710
15 20168
16 20074
17 20183
18 20202
19
Quantifying Time-Series Predictability through Structural Complexity.
20141
20 20071

About Ryan G. James

Ryan G. James is a scholar working on Statistical and Nonlinear Physics, Artificial Intelligence, Computational Theory and Mathematics, Atomic and Molecular Physics, and Optics and Cognitive Neuroscience, having authored 21 papers that have together received 464 indexed citations. Recurring topics across this work include Neural Networks and Applications (6 papers), Complex Network Analysis Techniques (4 papers), Neural dynamics and brain function (4 papers), Complex Systems and Time Series Analysis (3 papers), Time Series Analysis and Forecasting (3 papers), Opinion Dynamics and Social Influence (3 papers), Computability, Logic, AI Algorithms (3 papers) and Physics of Superconductivity and Magnetism (2 papers). The work is most often cited by research in Statistical and Nonlinear Physics (162 citations), Cognitive Neuroscience (137 citations), Artificial Intelligence (106 citations), Computational Theory and Mathematics (48 citations) and Economics and Econometrics (74 citations). Ryan G. James has collaborated with scholars based in United States, France and United Kingdom. Frequent co-authors include James P. Crutchfield, Christopher J. Ellison, Joshua Garland, Elizabeth Bradley, W. J. Stronge, Bahram Ravani, John R. Mahoney, Virgil Griffith, Edwin K. P. Chong and Ahmed H. Kissebah. Their work appears in journals such as Physical review. E, Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences, IEEE Transactions on Applied Superconductivity, Journal of Clinical Investigation and Journal of Applied Physics.

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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