Michael Schweinberger

1.7k total citations
27 papers, 847 citations indexed

About

Michael Schweinberger is a scholar working on Statistical and Nonlinear Physics, Artificial Intelligence and Statistics and Probability. According to data from OpenAlex, Michael Schweinberger has authored 27 papers receiving a total of 847 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Statistical and Nonlinear Physics, 10 papers in Artificial Intelligence and 7 papers in Statistics and Probability. Recurrent topics in Michael Schweinberger's work include Complex Network Analysis Techniques (16 papers), Opinion Dynamics and Social Influence (8 papers) and Bayesian Methods and Mixture Models (7 papers). Michael Schweinberger is often cited by papers focused on Complex Network Analysis Techniques (16 papers), Opinion Dynamics and Social Influence (8 papers) and Bayesian Methods and Mixture Models (7 papers). Michael Schweinberger collaborates with scholars based in United States, Netherlands and United Kingdom. Michael Schweinberger's co-authors include Tom A. B. Snijders, Mark Huisman, Christian Steglich, Johan Koskinen, Mark S. Handcock, David R. Hunter, Pavel N. Krivitsky, Ruth M. Ripley, Duy Vu and Michał Bojanowski and has published in prestigious journals such as Journal of the American Statistical Association, Journal of the Royal Statistical Society Series B (Statistical Methodology) and The Annals of Statistics.

In The Last Decade

Michael Schweinberger

23 papers receiving 776 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michael Schweinberger United States 14 426 227 124 116 86 27 847
Patrick Doreian United States 11 399 0.9× 265 1.2× 81 0.7× 43 0.4× 9 0.1× 24 744
Alberto Caimo Ireland 11 151 0.4× 76 0.3× 94 0.8× 34 0.3× 84 1.0× 22 460
Hansjörg Neth Germany 10 112 0.3× 151 0.7× 101 0.8× 43 0.4× 29 0.3× 38 644
Jana Diesner United States 15 280 0.7× 211 0.9× 376 3.0× 31 0.3× 7 0.1× 82 976
Jeffrey R. Travers United States 7 481 1.1× 258 1.1× 121 1.0× 59 0.5× 8 0.1× 15 1.1k
Alexander Volfovsky United States 10 282 0.7× 770 3.4× 228 1.8× 13 0.1× 31 0.4× 30 1.2k
Giancarlo Ragozini Italy 15 53 0.1× 143 0.6× 58 0.5× 82 0.7× 33 0.4× 50 551
Jan Sprenger Netherlands 16 31 0.1× 80 0.4× 185 1.5× 78 0.7× 85 1.0× 65 736
Rainer Hegselmann Germany 14 340 0.8× 350 1.5× 50 0.4× 21 0.2× 6 0.1× 35 897
Roel Popping Netherlands 11 55 0.1× 219 1.0× 143 1.2× 24 0.2× 11 0.1× 49 825

Countries citing papers authored by Michael Schweinberger

Since Specialization
Citations

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

Fields of papers citing papers by Michael Schweinberger

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michael Schweinberger

This figure shows the co-authorship network connecting the top 25 collaborators of Michael Schweinberger. A scholar is included among the top collaborators of Michael Schweinberger 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 Michael Schweinberger. Michael Schweinberger 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.
Holan, Scott H., et al.. (2025). A Socio‐demographic Latent Space Approach to Spatial Data When Geography Is Important But not All‐Important 1. International Statistical Review. 93(3). 351–373.
2.
Mele, Angelo, et al.. (2024). A Strategic Model of Software Dependency Networks. 863–892. 1 indexed citations
3.
4.
Schweinberger, Michael, et al.. (2024). Non-asymptotic model selection for models of network data with parameter vectors of increasing dimension. Journal of Statistical Planning and Inference. 233. 106173–106173.
5.
Jeon, Minjeong, et al.. (2022). Multilevel Network Item Response Modelling for Discovering Differences between Innovation and Regular School Systems in Korea. Journal of the Royal Statistical Society Series C (Applied Statistics). 71(5). 1225–1244.
6.
Schweinberger, Michael, et al.. (2020). Concentration and consistency results for canonical and curved exponential-family models of random graphs. The Annals of Statistics. 48(1). 12 indexed citations
7.
Schweinberger, Michael. (2020). Statistical inference for continuous‐time Markov processes with block structure based on discrete‐time network data. Statistica Neerlandica. 74(3). 342–362. 3 indexed citations
8.
Schweinberger, Michael, et al.. (2020). Large-scale estimation of random graph models with local dependence. Computational Statistics & Data Analysis. 152. 107029–107029. 7 indexed citations
9.
Schweinberger, Michael, et al.. (2019). Multilevel network data facilitate statistical inference for curved ERGMs with geometrically weighted terms. Social Networks. 59. 98–119. 21 indexed citations
10.
Schweinberger, Michael, Pavel N. Krivitsky, & Carter T. Butts. (2017). Foundations of Finite-, Super-, and Infinite-Population Random Graph Inference. 5 indexed citations
11.
Schweinberger, Michael, et al.. (2016). High-Dimensional Multivariate Time Series With Additional Structure. Journal of Computational and Graphical Statistics. 26(3). 610–622. 7 indexed citations
12.
Schweinberger, Michael, Miruna Petrescu‐Prahova, & Duy Vu. (2014). Disaster response on September 11, 2001 through the lens of statistical network analysis. Social Networks. 37. 42–55. 15 indexed citations
13.
Schweinberger, Michael & Mark S. Handcock. (2014). Local Dependence in Random Graph Models: Characterization, Properties and Statistical Inference. Journal of the Royal Statistical Society Series B (Statistical Methodology). 77(3). 647–676. 62 indexed citations
14.
Vu, Duy, David R. Hunter, & Michael Schweinberger. (2012). MODEL-BASED CLUSTERING OF LARGE NETWORKS1. 35 indexed citations
15.
Hunter, David R., Pavel N. Krivitsky, & Michael Schweinberger. (2012). Computational Statistical Methods for Social Network Models. Journal of Computational and Graphical Statistics. 21(4). 856–882. 54 indexed citations
16.
Schweinberger, Michael. (2011). Statistical modelling of network panel data: Goodness of fit. British Journal of Mathematical and Statistical Psychology. 65(2). 263–281. 68 indexed citations
17.
Schweinberger, Michael. (2011). Instability, Sensitivity, and Degeneracy of Discrete Exponential Families. Journal of the American Statistical Association. 106(496). 1361–1370. 84 indexed citations
18.
Snijders, Tom A. B., Johan Koskinen, & Michael Schweinberger. (2010). Maximum likelihood estimation for social network dynamics. The Annals of Applied Statistics. 4(2). 567–588. 102 indexed citations
19.
Schweinberger, Michael, et al.. (2010). Assessing and accounting for time heterogeneity in stochastic actor oriented models. Advances in Data Analysis and Classification. 5(2). 147–176. 69 indexed citations
20.
Snijders, Tom A. B., Christian Steglich, Michael Schweinberger, & Mark Huisman. (2005). Manual for SIENA version 2.1. University of Groningen research database (University of Groningen / Centre for Information Technology). 175 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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