Mikko Kivelä
- Statistical and Nonlinear Physics top 0.1%
- Computer Networks and Communications top 2%
- Sociology and Political Science top 2%
- Artificial Intelligence top 5%
- Molecular Biology
- Co-authors
- Mason A. PorterÀlex ArenasYamir MorenoJames P. GleesonMarc BarthélemyJari SaramäkiKimmo KaskiJános Kertész
- Topics
- Complex Network Analysis Techniques (34 papers)Opinion Dynamics and Social Influence (32 papers)Human Mobility and Location-Based Analysis (8 papers)
- Journals
- Proceedings of the National Academy of SciencesNature CommunicationsSHILAP Revista de lepidopterología
- Partner nations
- FinlandUnited KingdomFrance
In The Last Decade
Mikko Kivelä
48 papers receiving 3.9k citations
Hit Papers
Peers
Comparison fields: 5 of 177
- Statistical and Nonlinear Physics 2.4k
- Computer Networks and Communications 667
- Sociology and Political Science 574
- Artificial Intelligence 465
- Molecular Biology 430
Countries citing papers authored by Mikko Kivelä
This map shows the geographic impact of Mikko Kivelä'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 Mikko Kivelä with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mikko Kivelä more than expected).
Fields of papers citing papers by Mikko Kivelä
This network shows the impact of papers produced by Mikko Kivelä. 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 Mikko Kivelä. The network helps show where Mikko Kivelä may publish in the future.
Co-authorship network of co-authors of Mikko Kivelä
This figure shows the co-authorship network connecting the top 25 collaborators of Mikko Kivelä. A scholar is included among the top collaborators of Mikko Kivelä 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 Mikko Kivelä. Mikko Kivelä is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 3 | |
| 4 | 1 | |
| 5 | 0 | |
| 6 | 6 | |
| 7 | 8 | |
| 8 | 7 | |
| 9 | 24 | |
| 10 | 32 | |
| 11 | 13 | |
| 12 | 59 | |
| 13 | 1 | |
| 14 | 14 | |
| 15 | Mapping temporal-network percolation to weighted, static event graphs | 23 |
| 16 | Clustering Coefficients in Multiplex Networks. | 14 |
| 17 | 33 | |
| 18 | 173 | |
| 19 | A comparative study of stochastic algorithmic models for social networks | 1 |
| 20 | 461 |
About Mikko Kivelä
Mikko Kivelä is a scholar working on Statistical and Nonlinear Physics, Modeling and Simulation and Transportation, having authored 50 papers that have together received 4.0k indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (34 papers), Opinion Dynamics and Social Influence (32 papers) and Human Mobility and Location-Based Analysis (8 papers). The work is most often cited by research in Statistical and Nonlinear Physics (2.4k citations), Modeling and Simulation (198 citations) and Transportation (239 citations). Mikko Kivelä has collaborated with scholars based in Finland, United Kingdom and France. Frequent co-authors include Mason A. Porter, Àlex Arenas, Yamir Moreno, James P. Gleeson, Marc Barthélemy, Jari Saramäki, Kimmo Kaski, János Kertész, Jukka‐Pekka Onnela and Márton Karsai. Their work appears in journals such as Proceedings of the National Academy of Sciences, Nature Communications and SHILAP Revista de lepidopterología.
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.