Federico Cecconi

32 papers receiving 1.9k citations

Hit Papers

Defining and identifying communities in networks2004202620112018200450010001.5k

Peers

Federico Cecconi
Comparison fields: 5 of 143
  • Statistical and Nonlinear Physics 1.2k
  • Artificial Intelligence 455
  • Molecular Biology 419
  • Computer Networks and Communications 291
  • Sociology and Political Science 221
Replace Tiago P. Peixoto with:
Tiago P. Peixoto Germany
E. A. Leicht United States
Matthieu Latapy France
Pietro Panzarasa United Kingdom
Diego Garlaschelli Italy
Jierui Xie United States
Zhan Bu China
Martin Hoefer Germany
Albert Solé‐Ribalta Spain
Marco Gaertler Germany
Federico Cecconi relative to Tiago P. Peixoto Germany Tiago P. Peixoto's profile →
Citations per field
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Tiago P. Peixoto · 1×
Citations per year

Countries citing papers authored by Federico Cecconi

Since Specialization
Citations

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

Fields of papers citing papers by Federico Cecconi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Federico Cecconi

This figure shows the co-authorship network connecting the top 25 collaborators of Federico Cecconi. A scholar is included among the top collaborators of Federico Cecconi 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 Federico Cecconi. Federico Cecconi 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
#WorkIndexed citations
1 1
2 1
3 1
4 1
5 5
6 2
7 4
8 4
9 21
10
Low correlations between dividends and returns: the Alitalia 's case
1
11 7
12
How Agents Find out Norms: A Simulation Based Model of Norm Innovation.
12
13
Asymmetric pricing: an agent based model
2
14
Recurrent and concurrent neural networks for objects recognition
3
15 88
16
Defining and identifying communities in networksbreakdown →
1575
17 36
18 29
19 12
20
Neural networks with motivational units
7

About Federico Cecconi

Federico Cecconi is a scholar working on Statistical and Nonlinear Physics, Economics and Econometrics and Cognitive Neuroscience, having authored 33 papers that have together received 2.0k indexed citations. Recurring topics across this work include Complex Systems and Time Series Analysis (5 papers), Neural dynamics and brain function (4 papers) and Complex Network Analysis Techniques (4 papers). The work is most often cited by research in Statistical and Nonlinear Physics (1.2k citations), Artificial Intelligence (455 citations) and Computer Networks and Communications (291 citations). Federico Cecconi has collaborated with scholars based in Italy, United States and United Kingdom. Frequent co-authors include Domenico Parisi, Vittorio Loreto, Claudio Castellano, Filippo Radicchi, Stefano Nolfi, Domenico Parisi, Alain Barrat, Marco Campennì, Rosaria Conte and Giulia Andrighetto. Their work appears in journals such as Proceedings of the National Academy of Sciences, Analytical and Bioanalytical Chemistry and Frontiers in Neuroscience.

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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