Mark E. J. Newman
Impact in
- Statistical and Nonlinear Physics top 0.01%
- Complex Network Analysis Techniques
- Opinion Dynamics and Social Influence
- Modeling and Simulation top 0.05%
Papers in
-
- Complex Network Analysis Techniques 120
- Opinion Dynamics and Social Influence 78
-
- Theoretical and Computational Physics 28
- Co-authors
- Michelle Girvan (4 shared papers)Aaron J. Clauset (5 shared papers)Cristopher Moore (11 shared papers)Duncan J. Watts (7 shared papers)Steven H. Strogatz (5 shared papers)Brian Karrer (9 shared papers)G. T. Barkema (10 shared papers)Juyong M. Park (6 shared papers)
- Journals
- Physical review. E (13 papers)Proceedings of the National Academy of Sciences (11 papers)Physical Review Letters (11 papers)Networks (9 papers)Proceedings of the Royal Society B Biological Sciences (6 papers)
- Partner nations
- United StatesUnited KingdomNetherlands
In The Last Decade
Mark E. J. Newman
193 papers receiving 88.6k citations
Mark E. J. Newman's Hit Papers
Peers
Comparison fields: 5 of 246
- Statistical and Nonlinear Physics 55.3k
- Modeling and Simulation 2.7k
- Computer Networks and Communications 12.4k
- Transportation 3.4k
- Artificial Intelligence 16.0k
Countries citing papers authored by Mark E. J. Newman
This map shows the geographic impact of Mark E. J. Newman'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 Mark E. J. Newman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark E. J. Newman more than expected).
Fields of papers citing papers by Mark E. J. Newman
This network shows the impact of papers produced by Mark E. J. Newman. 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 Mark E. J. Newman. The network helps show where Mark E. J. Newman may publish in the future.
Co-authors
The 25 scholars most cited alongside Mark E. J. Newman, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 197 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Community structure in social and biological networks Hit paper breakdown → | 2002 | 11381 |
| 2 | Modularity and community structure in networks Hit paper breakdown → | 2006 | 8903 |
| 3 | Finding community structure in very large networks Hit paper breakdown → | 2004 | 5455 |
| 4 | Fast algorithm for detecting community structure in networks Hit paper breakdown → | 2004 | 4100 |
| 5 | Assortative Mixing in Networks Hit paper breakdown → | 2002 | 3680 |
| 6 | Finding community structure in networks using the eigenvectors of matrices Hit paper breakdown → | 2006 | 3522 |
| 7 | The structure of scientific collaboration networks Hit paper breakdown → | 2001 | 3330 |
| 8 | Random graphs with arbitrary degree distributions and their applications Hit paper breakdown → | 2001 | 2590 |
| 9 | Spread of epidemic disease on networks Hit paper breakdown → | 2002 | 2372 |
| 10 | Mixing patterns in networks Hit paper breakdown → | 2003 | 2180 |
| 11 | Monte Carlo Methods in Statistical Physics Hit paper breakdown → | 1999 | 1988 |
| 12 | Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality Hit paper breakdown → | 2001 | 1928 |
| 13 | Network Robustness and Fragility: Percolation on Random Graphs Hit paper breakdown → | 2000 | 1853 |
| 14 | Analysis of weighted networks Hit paper breakdown → | 2004 | 1849 |
| 15 | Power-law distributions in empirical data Hit paper breakdown → | 2018 | 1824 |
| 16 | A measure of betweenness centrality based on random walks Hit paper breakdown → | 2005 | 1786 |
| 17 | Networks Hit paper breakdown → | 2018 | 1592 |
| 18 | Hierarchical structure and the prediction of missing links in networks Hit paper breakdown → | 2008 | 1575 |
| 19 | Scientific collaboration networks. I. Network construction and fundamental results Hit paper breakdown → | 2001 | 1544 |
| 20 | Clustering and preferential attachment in growing networks Hit paper breakdown → | 2001 | 1500 |
About Mark E. J. Newman
Mark E. J. Newman is a scholar working on Statistical and Nonlinear Physics, Condensed Matter Physics, Geometry and Topology, Mathematical Physics and Paleontology, having authored 197 papers that have together received 93.2k indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (120 papers), Opinion Dynamics and Social Influence (78 papers), Theoretical and Computational Physics (28 papers), Bioinformatics and Genomic Networks (23 papers), Graph theory and applications (16 papers), Complex Systems and Time Series Analysis (15 papers), Evolutionary Game Theory and Cooperation (14 papers) and Stochastic processes and statistical mechanics (13 papers). The work is most often cited by research in Statistical and Nonlinear Physics (55.3k citations), Modeling and Simulation (2.7k citations), Computer Networks and Communications (12.4k citations), Transportation (3.4k citations) and Artificial Intelligence (16.0k citations). Mark E. J. Newman has collaborated with scholars based in United States, United Kingdom and Netherlands. Frequent co-authors include Michelle Girvan, Aaron J. Clauset, Cristopher Moore, Duncan J. Watts, Steven H. Strogatz, Brian Karrer, G. T. Barkema, Juyong M. Park, E. A. Leicht and Cosma Rohilla Shalizi. Their work appears in journals such as Physical review. E, Proceedings of the National Academy of Sciences, Physical Review Letters, Networks and Proceedings of the Royal Society B Biological Sciences.
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.