M. 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 126
- Opinion Dynamics and Social Influence 82
-
- Theoretical and Computational Physics 28
- Co-authors
- Michelle Girvan (4 shared papers)Aaron Clauset (5 shared papers)Cristopher Moore (11 shared papers)Duncan J. Watts (6 shared papers)Steven H. Strogatz (5 shared papers)Brian Karrer (9 shared papers)G. T. Barkema (10 shared papers)Juyong Park (6 shared papers)
- Journals
- Physical review. E (13 papers)Physical Review Letters (11 papers)Proceedings of the National Academy of Sciences (10 papers)Networks (9 papers)Proceedings of the Royal Society B Biological Sciences (6 papers)
- Partner nations
- United StatesUnited KingdomNetherlands
In The Last Decade
M. E. J. Newman
197 papers receiving 87.4k citations
M. E. J. Newman's Hit Papers
Peers
Comparison fields: 5 of 246
- Statistical and Nonlinear Physics 54.8k
- Modeling and Simulation 2.7k
- Computer Networks and Communications 12.4k
- Transportation 3.4k
- Artificial Intelligence 15.7k
Countries citing papers authored by M. E. J. Newman
This map shows the geographic impact of M. 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 M. 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 M. E. J. Newman more than expected).
Fields of papers citing papers by M. E. J. Newman
This network shows the impact of papers produced by M. 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 M. E. J. Newman. The network helps show where M. E. J. Newman may publish in the future.
Co-authors
The 25 scholars most cited alongside M. 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 201 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 | 11252 |
| 2 | Modularity and community structure in networks Hit paper breakdown → | 2006 | 8723 |
| 3 | Finding community structure in very large networks Hit paper breakdown → | 2004 | 5377 |
| 4 | Fast algorithm for detecting community structure in networks Hit paper breakdown → | 2004 | 4064 |
| 5 | Assortative Mixing in Networks Hit paper breakdown → | 2002 | 3646 |
| 6 | Finding community structure in networks using the eigenvectors of matrices Hit paper breakdown → | 2006 | 3459 |
| 7 | The structure of scientific collaboration networks Hit paper breakdown → | 2001 | 3332 |
| 8 | Random graphs with arbitrary degree distributions and their applications Hit paper breakdown → | 2001 | 2602 |
| 9 | Spread of epidemic disease on networks Hit paper breakdown → | 2002 | 2325 |
| 10 | Mixing patterns in networks Hit paper breakdown → | 2003 | 2166 |
| 11 | Monte Carlo Methods in Statistical Physics Hit paper breakdown → | 1999 | 1979 |
| 12 | Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality Hit paper breakdown → | 2001 | 1920 |
| 13 | Network Robustness and Fragility: Percolation on Random Graphs Hit paper breakdown → | 2000 | 1842 |
| 14 | Analysis of weighted networks Hit paper breakdown → | 2004 | 1835 |
| 15 | Power-law distributions in empirical data Hit paper breakdown → | 2018 | 1807 |
| 16 | A measure of betweenness centrality based on random walks Hit paper breakdown → | 2005 | 1759 |
| 17 | Scientific collaboration networks. I. Network construction and fundamental results Hit paper breakdown → | 2001 | 1556 |
| 18 | Hierarchical structure and the prediction of missing links in networks Hit paper breakdown → | 2008 | 1552 |
| 19 | Networks Hit paper breakdown → | 2018 | 1501 |
| 20 | Clustering and preferential attachment in growing networks Hit paper breakdown → | 2001 | 1478 |
About M. E. J. Newman
M. E. J. Newman is a scholar working on Statistical and Nonlinear Physics, Condensed Matter Physics, Molecular Biology, Geometry and Topology and Computer Networks and Communications, having authored 201 papers that have together received 92.3k indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (126 papers), Opinion Dynamics and Social Influence (82 papers), Theoretical and Computational Physics (28 papers), Bioinformatics and Genomic Networks (21 papers), Graph theory and applications (17 papers), Stochastic processes and statistical mechanics (16 papers), Evolutionary Game Theory and Cooperation (14 papers) and Complex Systems and Time Series Analysis (13 papers). The work is most often cited by research in Statistical and Nonlinear Physics (54.8k citations), Modeling and Simulation (2.7k citations), Computer Networks and Communications (12.4k citations), Transportation (3.4k citations) and Artificial Intelligence (15.7k citations). M. E. J. Newman has collaborated with scholars based in United States, United Kingdom and Netherlands. Frequent co-authors include Michelle Girvan, Aaron Clauset, Cristopher Moore, Duncan J. Watts, Steven H. Strogatz, Brian Karrer, G. T. Barkema, Juyong Park, E. A. Leicht and Cosma Rohilla Shalizi. Their work appears in journals such as Physical review. E, Physical Review Letters, Proceedings of the National Academy of Sciences, 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.