Diego Garlaschelli

6.2k total citations · 1 hit paper
85 papers, 3.4k citations indexed

About

Diego Garlaschelli is a scholar working on Statistical and Nonlinear Physics, Economics and Econometrics and Computational Theory and Mathematics. According to data from OpenAlex, Diego Garlaschelli has authored 85 papers receiving a total of 3.4k indexed citations (citations by other indexed papers that have themselves been cited), including 61 papers in Statistical and Nonlinear Physics, 44 papers in Economics and Econometrics and 17 papers in Computational Theory and Mathematics. Recurrent topics in Diego Garlaschelli's work include Complex Network Analysis Techniques (53 papers), Complex Systems and Time Series Analysis (30 papers) and Opinion Dynamics and Social Influence (19 papers). Diego Garlaschelli is often cited by papers focused on Complex Network Analysis Techniques (53 papers), Complex Systems and Time Series Analysis (30 papers) and Opinion Dynamics and Social Influence (19 papers). Diego Garlaschelli collaborates with scholars based in Italy, Netherlands and United Kingdom. Diego Garlaschelli's co-authors include Maria I. Loffredo, Tiziano Squartini, Guido Caldarelli, Giorgio Fagiolo, Stefano Battiston, L. Pietronero, Giulio Cimini, Fabio Saracco, Andrea Gabrielli and Matteo Barigozzi and has published in prestigious journals such as Nature, Science and Proceedings of the National Academy of Sciences.

In The Last Decade

Diego Garlaschelli

75 papers receiving 3.3k citations

Hit Papers

Complexity theory and financial regulation 2016 2026 2019 2022 2016 100 200 300
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.

  1. Complexity theory and financial regulation
    2016 316 citations Stefano Battiston, Diego Garlaschelli et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Diego Garlaschelli Italy 28 1.8k 1.4k 325 315 287 85 3.4k
Giorgio Fagiolo Italy 40 1.5k 0.8× 4.0k 2.8× 878 2.7× 681 2.2× 133 0.5× 121 6.3k
Sorin Solomon Israel 29 753 0.4× 1.3k 0.9× 426 1.3× 337 1.1× 146 0.5× 85 2.7k
Janusz A. Hołyst Poland 29 1.9k 1.1× 683 0.5× 148 0.5× 527 1.7× 132 0.5× 152 3.2k
Alexander M. Petersen United States 23 903 0.5× 1.1k 0.8× 349 1.1× 454 1.4× 219 0.8× 75 3.3k
Dror Y. Kenett United States 28 994 0.6× 1.4k 1.0× 651 2.0× 224 0.7× 145 0.5× 54 2.7k
Francisco A. Rodrigues Brazil 28 2.2k 1.2× 529 0.4× 62 0.2× 416 1.3× 561 2.0× 131 5.0k
M. Ángeles Serrano Spain 29 2.7k 1.5× 476 0.3× 29 0.1× 417 1.3× 568 2.0× 69 4.2k
Tore Opsahl United Kingdom 11 1.6k 0.9× 312 0.2× 47 0.1× 587 1.9× 308 1.1× 15 4.2k
Boris Podobnik Croatia 34 2.5k 1.4× 4.7k 3.3× 2.1k 6.4× 613 1.9× 380 1.3× 112 6.6k
Tiziano Squartini Italy 18 644 0.4× 484 0.3× 142 0.4× 130 0.4× 79 0.3× 56 1.4k

Countries citing papers authored by Diego Garlaschelli

Since Specialization
Citations

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

Fields of papers citing papers by Diego Garlaschelli

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Diego Garlaschelli

This figure shows the co-authorship network connecting the top 25 collaborators of Diego Garlaschelli. A scholar is included among the top collaborators of Diego Garlaschelli 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 Diego Garlaschelli. Diego Garlaschelli 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.
Saracco, Fabio, et al.. (2025). Patterns of link reciprocity in directed, signed networks. Physical review. E. 111(2). 24312–24312. 2 indexed citations
2.
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Garlaschelli, Diego, et al.. (2024). Testing structural balance theories in heterogeneous signed networks. Communications Physics. 7(1). 11 indexed citations
5.
Garlaschelli, Diego, et al.. (2024). Assessing frustration in real-world signed networks: A statistical theory of balance. Physical Review Research. 6(4). 2 indexed citations
6.
Neal, Zachary P., Diego Garlaschelli, Nicholas J. Gotelli, et al.. (2024). Pattern detection in bipartite networks: A review of terminology, applications, and methods. Leiden Repository (Leiden University). 1(2). e0000010–e0000010. 7 indexed citations
7.
Brintrup, Alexandra, et al.. (2024). Reconstructing supply networks. Journal of Physics Complexity. 5(1). 12001–12001. 3 indexed citations
8.
Ferragina, Paolo, et al.. (2024). On Nonlinear Compression Costs: When Shannon Meets Rényi. IEEE Access. 12. 77750–77763.
9.
Garlaschelli, Diego, et al.. (2023). Central limit theorem for the principal eigenvalue and eigenvector of Chung–Lu random graphs. Journal of Physics Complexity. 4(1). 15008–15008.
10.
Garlaschelli, Diego, et al.. (2023). Multiscale network renormalization: Scale-invariance without geometry. Physical Review Research. 5(4). 7 indexed citations
11.
Garlaschelli, Diego, et al.. (2023). Deterministic, quenched, and annealed parameter estimation for heterogeneous network models. Physical review. E. 108(5).
12.
Caruso, Tancredi, et al.. (2022). Fluctuating ecological networks: A synthesis of maximum‐entropy approaches for pattern detection and process inference. Methods in Ecology and Evolution. 13(11). 2306–2317. 8 indexed citations
13.
Jansen, F., et al.. (2022). Reconstructing firm-level interactions in the Dutch input–output network from production constraints. Scientific Reports. 12(1). 11847–11847. 14 indexed citations
14.
Garlaschelli, Diego, et al.. (2022). Reconciling econometrics with continuous maximum-entropy network models. Chaos Solitons & Fractals. 166. 112958–112958. 1 indexed citations
15.
Bardoscia, Marco, Paolo Barucca, Stefano Battiston, et al.. (2021). The physics of financial networks. Nature Reviews Physics. 3(7). 490–507. 135 indexed citations
16.
Squartini, Tiziano, Andrea Gabrielli, Diego Garlaschelli, et al.. (2018). Complexity in Neural and Financial Systems: From Time‐Series to Networks. Complexity. 2018(1). 7 indexed citations
17.
Squartini, Tiziano, Giulio Cimini, Andrea Gabrielli, & Diego Garlaschelli. (2017). Network reconstruction via density sampling. Applied Network Science. 2(1). 3–3. 9 indexed citations
18.
Garlaschelli, Diego, et al.. (2013). Unbiased community detection for correlation matrices. arXiv (Cornell University). 2 indexed citations
19.
Squartini, Tiziano, Giorgio Fagiolo, & Diego Garlaschelli. (2011). Rewiring World Trade. Part I: A Binary Network Analysis. Econstor (Econstor). 3 indexed citations
20.
Buchanan, Mark, Paolo De Los Rios, Sarath Chandra Janga, et al.. (2010). Networks in Cell Biology. Cambridge University Press eBooks. 36 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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