Matthew Salganik

31 papers receiving 4.9k citations

Matthew Salganik's Hit Papers

Integrating explanation and prediction in computational social science 2021 · 205 citations
2050+7+14Years since publication4008001.2k

Peers

Matthew Salganik
Comparison fields: 5 of 176
  • Statistical and Nonlinear Physics 890
  • Communication 379
  • Infectious Diseases 864
  • Sociology and Political Science 2.0k
  • Epidemiology 1.4k
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Citations per year

Countries citing papers authored by Matthew Salganik

Since Specialization
Citations

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

Fields of papers citing papers by Matthew Salganik

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 31 papers — load more, or switch the sort, to bring in the rest.

#Work
1
5. Sampling and Estimation in Hidden Populations Using Respondent-Driven Sampling
Hit paper breakdown →
20041436
2
Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market
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20061320
3 2006330
4 2010279
5
Computational social science: Obstacles and opportunities
Hit paper breakdown →
2020243
6
Integrating explanation and prediction in computational social science
Hit paper breakdown →
2021205
7 2014181
8 2008177
9 2015171
10 2009144
11 2006132
12 2010129
13 201590
14 200980
15 201177
16 201056
17 201655
18 202437
19 201631
20 201928

About Matthew Salganik

Matthew Salganik is a scholar working on Epidemiology, Infectious Diseases, Statistical and Nonlinear Physics, Sociology and Political Science and Artificial Intelligence, having authored 31 papers that have together received 5.3k indexed citations. Recurring topics across this work include HIV, Drug Use, Sexual Risk (11 papers), HIV/AIDS Research and Interventions (9 papers), Complex Network Analysis Techniques (6 papers), Computational and Text Analysis Methods (5 papers), Explainable Artificial Intelligence (XAI) (5 papers), Opinion Dynamics and Social Influence (4 papers), Scientific Computing and Data Management (3 papers) and Experimental Behavioral Economics Studies (2 papers). The work is most often cited by research in Statistical and Nonlinear Physics (890 citations), Communication (379 citations), Infectious Diseases (864 citations), Sociology and Political Science (2.0k citations) and Epidemiology (1.4k citations). Matthew Salganik has collaborated with scholars based in United States, United Kingdom and Switzerland. Frequent co-authors include Douglas D. Heckathorn, Duncan J. Watts, Peter Sheridan Dodds, Sharad Goel, Tian Zheng, Lisa G. Johnston, Krista J. Gile, Karen Levy, Andrew Gelman and Tyler H. McCormick. Their work appears in journals such as Socius Sociological Research for a Dynamic World, Sociological Methodology, Science, Proceedings of the National Academy of Sciences and American Journal of Epidemiology.

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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