Chris J. Kuhlman

1.3k total citations
76 papers, 587 citations indexed

About

Chris J. Kuhlman is a scholar working on Statistical and Nonlinear Physics, Management Science and Operations Research and Sociology and Political Science. According to data from OpenAlex, Chris J. Kuhlman has authored 76 papers receiving a total of 587 indexed citations (citations by other indexed papers that have themselves been cited), including 43 papers in Statistical and Nonlinear Physics, 14 papers in Management Science and Operations Research and 11 papers in Sociology and Political Science. Recurrent topics in Chris J. Kuhlman's work include Complex Network Analysis Techniques (39 papers), Opinion Dynamics and Social Influence (37 papers) and COVID-19 epidemiological studies (8 papers). Chris J. Kuhlman is often cited by papers focused on Complex Network Analysis Techniques (39 papers), Opinion Dynamics and Social Influence (37 papers) and COVID-19 epidemiological studies (8 papers). Chris J. Kuhlman collaborates with scholars based in United States, Italy and Ecuador. Chris J. Kuhlman's co-authors include S. S. Ravi, Madhav Marathe, Gizem Korkmaz, Anil Vullikanti, José Cadena, Achla Marathe, Naren Ramakrishnan, Henning Mortveit, Samarth Swarup and Abhijin Adiga and has published in prestigious journals such as PLoS ONE, International Journal of Impact Engineering and IBM Journal of Research and Development.

In The Last Decade

Chris J. Kuhlman

71 papers receiving 567 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Chris J. Kuhlman United States 15 250 87 75 70 67 76 587
Xiao-Pu Han China 15 364 1.5× 123 1.4× 40 0.5× 47 0.7× 11 0.2× 44 851
Yunhan Huang United States 11 91 0.4× 50 0.6× 60 0.8× 42 0.6× 27 0.4× 28 490
Yeliz Karaca United States 14 134 0.5× 27 0.3× 245 3.3× 81 1.2× 20 0.3× 74 839
Stephen C. Billups United States 13 65 0.3× 51 0.6× 49 0.7× 62 0.9× 34 0.5× 27 929
Aurelio La Corte Italy 14 101 0.4× 91 1.0× 32 0.4× 38 0.5× 61 0.9× 75 627
Meng Liu China 13 96 0.4× 67 0.8× 6 0.1× 300 4.3× 35 0.5× 53 764
Xin Guo China 15 45 0.2× 17 0.2× 13 0.2× 190 2.7× 25 0.4× 77 531
Edson Pindza South Africa 16 145 0.6× 12 0.1× 446 5.9× 25 0.4× 31 0.5× 67 807
Chung-Yuan Huang Taiwan 12 206 0.8× 62 0.7× 48 0.6× 32 0.5× 5 0.1× 53 431

Countries citing papers authored by Chris J. Kuhlman

Since Specialization
Citations

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

Fields of papers citing papers by Chris J. Kuhlman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chris J. Kuhlman

This figure shows the co-authorship network connecting the top 25 collaborators of Chris J. Kuhlman. A scholar is included among the top collaborators of Chris J. Kuhlman 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 Chris J. Kuhlman. Chris J. Kuhlman 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
1.
Hu, Zhihao, et al.. (2024). An uncertainty quantification framework for agent-based modeling and simulation in networked anagram games. Journal of Simulation. 18(4). 505–523. 1 indexed citations
2.
Liu, Xueying, Zhihao Hu, Xinwei Deng, & Chris J. Kuhlman. (2022). A Bayesian Uncertainty Quantification Approach for Agent-Based Modeling of Networked Anagram Games. 2022 Winter Simulation Conference (WSC). 112. 310–321. 2 indexed citations
3.
Kuhlman, Chris J., et al.. (2021). CSonNet: An Agent-Based Modeling Software System for Discrete Time Simulation. 1–12. 8 indexed citations
4.
Hu, Zhihao, Yihui Ren, Xinwei Deng, et al.. (2020). Networked experiments and modeling for producing collective identity in a group of human subjects using an iterative abduction framework. Social Network Analysis and Mining. 10(1). 13 indexed citations
5.
Kuhlman, Chris J., S. S. Ravi, Gizem Korkmaz, & Fernando Vega‐Redondo. (2020). An Agent-Based Model of Common Knowledge and Collective Action Dynamics on Social Networks. 79. 218–229.
6.
Adiga, Abhijin, Chris J. Kuhlman, Madhav Marathe, Ravi Samikannu, & Anil Vullikanti. (2019). PAC Learnability of Node Functions in Networked Dynamical Systems. International Conference on Machine Learning. 97. 82–91. 1 indexed citations
7.
Korkmaz, Gizem, et al.. (2018). Coordination and Common Knowledge on Communication Networks. Adaptive Agents and Multi-Agents Systems. 1062–1070. 4 indexed citations
8.
Kuhlman, Chris J., Yihui Ren, Bryan Lewis, & James Schlitt. (2017). Hybrid agent-based modeling of Zika in the United States. Winter Simulation Conference. 1085–1096. 3 indexed citations
9.
Adiga, Abhijin, Chris J. Kuhlman, Madhav Marathe, et al.. (2016). Inferring local transition functions of discrete dynamical systems from observations of system behavior. Theoretical Computer Science. 679. 126–144. 6 indexed citations
10.
Kuhlman, Chris J. & Henning Mortveit. (2015). Limit Sets of Generalized, Multi-Threshold Networks.. 10. 161–193. 3 indexed citations
11.
Kuhlman, Chris J., et al.. (2015). EDISON. 413–422. 2 indexed citations
12.
Korkmaz, Gizem, Chris J. Kuhlman, Achla Marathe, Madhav Marathe, & Fernando Vega‐Redondo. (2014). Collective action through common knowledge using a facebook model. Adaptive Agents and Multi-Agents Systems. 253–260. 12 indexed citations
13.
Kuhlman, Chris J. & Henning Mortveit. (2014). Attractor stability in nonuniform Boolean networks. Theoretical Computer Science. 559. 20–33. 6 indexed citations
14.
Tuli, Gaurav, Chris J. Kuhlman, Madhav Marathe, S. S. Ravi, & Daniel J. Rosenkrantz. (2012). Blocking complex contagions using community structure. 3 indexed citations
15.
Bisset, Keith, Jiangzhuo Chen, Chris J. Kuhlman, V. S. Anil Kumar, & Madhav Marathe. (2011). Interaction-based HPC modeling of social, biological, and economic contagions over large networks. 101. 2933–2947. 3 indexed citations
16.
McClung, R. Craig, et al.. (2009). DEVELOPMENT OF A PROBABILISTIC DESIGN SYSTEM FOR GAS TURBINE ROTOR INTEGRITY. 1 indexed citations
17.
Popelar, C. H. & Chris J. Kuhlman. (1997). Guidelines for installing PE gas pipes using HDD. 224(6). 21–22. 1 indexed citations
18.
Kuhlman, Chris J., et al.. (1993). A subcommittee review of the quality assurance initiative: implementation issues from the implementation and communication subcommittee of the drug residue committee.. 144–146. 1 indexed citations
19.
Anderson, Charles E., Scott A. Mullin, & Chris J. Kuhlman. (1993). Computer simulation of strain-rate effects in replica scale model penetration experiments. International Journal of Impact Engineering. 13(1). 35–52. 25 indexed citations
20.
Kuhlman, Chris J., Hüseyin Şehitoğlu, & Margaret C. Gallagher. (1988). THE SIGNIFICANCE OF MATERIAL PROPERTIES ON STRESSES DEVELOPED DURING QUENCHING OF RAILROAD WHEELS. 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.

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