Flaviano Morone

2.3k total citations · 1 hit paper
29 papers, 1.4k citations indexed

About

Flaviano Morone is a scholar working on Statistical and Nonlinear Physics, Condensed Matter Physics and Molecular Biology. According to data from OpenAlex, Flaviano Morone has authored 29 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Statistical and Nonlinear Physics, 6 papers in Condensed Matter Physics and 5 papers in Molecular Biology. Recurrent topics in Flaviano Morone's work include Complex Network Analysis Techniques (13 papers), Opinion Dynamics and Social Influence (9 papers) and Theoretical and Computational Physics (6 papers). Flaviano Morone is often cited by papers focused on Complex Network Analysis Techniques (13 papers), Opinion Dynamics and Social Influence (9 papers) and Theoretical and Computational Physics (6 papers). Flaviano Morone collaborates with scholars based in United States, Italy and Switzerland. Flaviano Morone's co-authors include Hernán A. Makse, Gino Del Ferraro, Alexandre Bovet, Byungjoon Min, Sen Pei, M. Silvina Tomassone, Kevin A. Roth, Carlos Sarraute, Giorgio Parisi and H. Eugene Stanley and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Nature Communications.

In The Last Decade

Flaviano Morone

28 papers receiving 1.4k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Influence maximization in complex networks through optimal percolation

2015 848 citations Flaviano Morone, Hernán A. Makse Nature profile →

Peers

Flaviano Morone

SNP

CNC

SPS

AI

MB

Guilherme Ferraz de Arruda Spain

Márton Karsai Hungary

Raj Kumar Pan Finland

Andrea Capocci Italy

Tim Evans United Kingdom

Konstantin Klemm Germany

Jean‐Charles Delvenne Belgium

Luca Dall’Asta Italy

Raúl J. Mondragón United Kingdom

Jie Zhou China

Guilherme Ferraz de Arruda Spain

Flaviano Morone

865 ×0.9

SNP

225 ×0.9

CNC

313 ×1.4

SPS

125 ×0.8

AI

144 ×1.0

MB

Citations per year, relative to Flaviano Morone Flaviano Morone (= 1×) peers Guilherme Ferraz de Arruda

Countries citing papers authored by Flaviano Morone

Since Specialization

Citations

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

Fields of papers citing papers by Flaviano Morone

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Flaviano Morone

This figure shows the co-authorship network connecting the top 25 collaborators of Flaviano Morone. A scholar is included among the top collaborators of Flaviano Morone 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 Flaviano Morone. Flaviano Morone is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

1.

Kent, Andrew D., et al.. (2025). Solving combinatorial optimization problems through stochastic Landau-Lifshitz-Gilbert dynamical systems. Physical Review Research. 7(1). 1 indexed citations

2.

Mariani, Manuel Sebastian, et al.. (2024). Equivalence between the Fitness-Complexity and the Sinkhorn-Knopp algorithms. Journal of Physics Complexity. 5(1). 15010–15010. 4 indexed citations

3.

Mariani, Manuel Sebastian, et al.. (2024). Ranking species in complex ecosystems through nestedness maximization. Communications Physics. 7(1). 1 indexed citations

4.

Morone, Flaviano & Dries Sels. (2023). Exact solution to the fully connected XY model with Gaussian random fields by the replica method. Physica A Statistical Mechanics and its Applications. 629. 129207–129207. 1 indexed citations

5.

Morone, Flaviano. (2022). Clustering matrices through optimal permutations. Journal of Physics Complexity. 3(3). 35007–35007. 3 indexed citations

6.

Morone, Flaviano, et al.. (2020). Circuits with broken fibration symmetries perform core logic computations in biological networks. PLoS Computational Biology. 16(6). e1007776–e1007776. 12 indexed citations

7.

Morone, Flaviano, et al.. (2020). K-core robustness in ecological and financial networks. Scientific Reports. 10(1). 3357–3357. 23 indexed citations

8.

Morone, Flaviano, et al.. (2020). Fibration symmetries uncover the building blocks of biological networks. Proceedings of the National Academy of Sciences. 117(15). 8306–8314. 30 indexed citations

9.

Ferraro, Gino Del, Andrea Moreno, Byungjoon Min, et al.. (2018). Finding influential nodes for integration in brain networks using optimal percolation theory. CUNY Academic Works (City University of New York). 61 indexed citations

10.

Bovet, Alexandre, Flaviano Morone, & Hernán A. Makse. (2018). Validation of Twitter opinion trends with national polling aggregates: Hillary Clinton vs Donald Trump. Scientific Reports. 8(1). 8673–8673. 67 indexed citations

11.

Morone, Flaviano, et al.. (2018). The jamming transition is a k-core percolation transition. Physica A Statistical Mechanics and its Applications. 516. 172–177. 15 indexed citations

12.

Pei, Sen, Flaviano Morone, Fredrik Liljeros, Hernán A. Makse, & Jeffrey Shaman. (2018). Inference and control of the nosocomial transmission of methicillin-resistant Staphylococcus aureus. eLife. 7. 29 indexed citations

13.

Morone, Flaviano, Gino Del Ferraro, & Hernán A. Makse. (2018). The k-core as a predictor of structural collapse in mutualistic ecosystems. Nature Physics. 15(1). 95–102. 102 indexed citations

14.

Morone, Flaviano, et al.. (2017). Collective Behaviour in Video Viewing: A Thermodynamic Analysis of Gaze Position. PLoS ONE. 12(1). e0168995–e0168995. 10 indexed citations

15.

Morone, Flaviano, et al.. (2017). Inferring personal economic status from social network location. Nature Communications. 8(1). 15227–15227. 48 indexed citations

16.

Morone, Flaviano, et al.. (2016). Collective influence maximization in threshold models of information cascading with first-order transitions.. arXiv (Cornell University). 2 indexed citations

17.

Morone, Flaviano, et al.. (2016). Efficient collective influence maximization in threshold models of behavior cascading with first-order transitions. arXiv (Cornell University). 2 indexed citations

18.

Pei, Sen, et al.. (2016). Collective Influence of Multiple Spreaders Evaluated by Tracing Real Information Flow in Large-Scale Social Networks. Scientific Reports. 6(1). 36043–36043. 44 indexed citations

19.

Morone, Flaviano & Hernán A. Makse. (2015). Influence maximization in complex networks through optimal percolation. Nature. 524(7563). 65–68. 848 indexed citations breakdown →

20.

Lucibello, Carlo, Flaviano Morone, Giorgio Parisi, Federico Ricci‐Tersenghi, & Tommaso Rizzo. (2014). Finite-size corrections to disordered Ising models on random regular graphs. Physical Review E. 90(1). 12146–12146. 15 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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