Michael Mäs

2.1k citations
30 papers · 1.2k · 1 hit paper · h-index 15

Impact in

Papers in

Michael Mäs

30 papers receiving 1.2k citations

Michael Mäs's Hit Papers

Models of Social Influence: Towards the Next Frontiers 2017 · 330 citations
3300+3+6Years since publication100200300

Peers

Michael Mäs
Comparison fields: 5 of 111
  • Statistical and Nonlinear Physics 704
  • Communication 241
  • Sociology and Political Science 549
  • Modeling and Simulation 44
  • Management Science and Operations Research 78
Replace Skyler Cranmer with:
Skyler Cranmer United States
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Emma S. Spiro United States
Devon Brackbill United States
Luke Sloan United Kingdom
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Philipp Lorenz-Spreen Germany
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Citations per year

Countries citing papers authored by Michael Mäs

Since Specialization
Citations

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

Fields of papers citing papers by Michael Mäs

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 30 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Models of Social Influence: Towards the Next Frontiers
Hit paper breakdown →
2017330
2 2013148
3 2010135
4 201287
5 201771
6 201665
7 201555
8 201649
9 200840
10 200731
11 201828
12 202227
13 201424
14 202220
15 200516
16 202013
17 20148
18 20226
19 20205
20 20165

About Michael Mäs

Michael Mäs is a scholar working on Sociology and Political Science, Statistical and Nonlinear Physics, Communication, Political Science and International Relations and Safety Research, having authored 30 papers that have together received 1.2k indexed citations. Recurring topics across this work include Opinion Dynamics and Social Influence (19 papers), Complex Network Analysis Techniques (9 papers), Social Media and Politics (8 papers), Evolutionary Game Theory and Cooperation (6 papers), Electoral Systems and Political Participation (4 papers), Social and Intergroup Psychology (4 papers), Misinformation and Its Impacts (4 papers) and Experimental Behavioral Economics Studies (3 papers). The work is most often cited by research in Statistical and Nonlinear Physics (704 citations), Communication (241 citations), Sociology and Political Science (549 citations), Modeling and Simulation (44 citations) and Management Science and Operations Research (78 citations). Michael Mäs has collaborated with scholars based in Netherlands, Germany and Switzerland. Frequent co-authors include Andreas Flache, Dirk Helbing, Jan Lorenz, Thomas Feliciani, Sylvie Huet, Edmund Chattoe‐Brown, Guillaume Deffuant, Károly Takács, Karen A. Jehn and Stefano Balietti. Their work appears in journals such as PLoS ONE, Journal of Artificial Societies and Social Simulation, Social Science Research, Computational and Mathematical Organization Theory and Social Networks.

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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