In the paper Defining and identifying the optimal embedding dimension of networks, we propose a principled method for the identification of an optimal dimension such that all structural information of a network is parsimoniously encoded. The python code used for this project is available here.

This material can be cited as

Defining and identifying the optimal embedding dimension of networks

W. Gu, A. Tandon, Y.Y. Ahn and **F. Radicchi**

arXiv:2004.09928 (2020) [pdf]

In the paper Classes of critical avalanche dynamics in complex networks, we study statistical properties of avalanches generated by several well-known dynamical processes on networks. Source code of the program used to simulate the various dynamical processes is available here.

This material can be cited as

Classes of critical avalanche dynamics in complex networks

**F. Radicchi**, C. Castellano, A. Flammini, M.A. MuĂ±oz and D. Notarmuzi

Phys. Rev. Research **2**, 033171 (2020) [pdf]

In the paper Uncertainty Reduction for Stochastic Processes on Complex Networks, we introduced a novel algorithm aimed at identifying the best nodes to observe in a network if the goal is to minimize the uncertainty of a stochastic dynamical process running on the network. A python implementation of the algorithm is available here.

This material can be cited as

Uncertainty Reduction for Stochastic Processes on Complex Networks
**F. Radicchi** and C. Castellano

Phys. Rev. Lett. **120**, 198301 (2018) [pdf]

We conducted a large-scale survey on the perceived impact of scientific publications taking advantage of the platform Big Science Survey.

This study can be cited as

Quantifying perceived impact of scientific publications

**F. Radicchi**, A. Weissman and J. Bollen

J. Informetr. **11**, 704-712 (2017) [pdf]

In the papers Percolation in real interdependent networks and Percolation in real multiplex networks, we introduced two methods to approximate the percolation phase diagram of interdependent networks. Code and data used in the papers can be downloaded here. Additional information can be found in the README file contained in the archive.

This material can be cited as

Percolation in real interdependent networks

**F. Radicchi**

Nat. Phys. **11**, 597-602 (2015) [pdf]

and

Percolation in real multiplex networks

G. Bianconi and **F. Radicchi**

Phys. Rev. E **94**, 060301(R) (2016) [pdf]

Tennis Prestige uses publicly available data about tennis matches to generate a weighted and directed network of contacts among players, and then measure their performance with Prestige Score, a variant of the well known PageRank centrality.

This ranking procedure can be cited as

Who is the best player ever? A complex network analysis of the history of professional tennis

**F. Radicchi**

PloS ONE **6**, e17249 (2011) [pdf]

In the paper Analysis of bibliometric indicators for individual scholars in a large data set, we analyzed a large data set of Google Scholar Citations profiles. The data used in the paper can be downloaded here. Additional information can be found here.

This material can be cited as

Analysis of bibliometric indicators for
individual scholars in a large data set

**F. Radicchi** and C. Castellano

Scientometrics **97**, 627 (2013) [pdf]

In the paper Universality, limits and predictability of gold-medal performances at the Olympic Games, we analyzed performance data of athletes at Olympic Games. The data used in the paper can be downloaded here.

This material can be cited as

Universality, limits and predictability of gold-medal performances at the Olympic Games

**F. Radicchi**

Plos ONE **7**, e40335 (2012) [pdf]

In the paper Rationality, irrationality and escalating behavior in lowest unique bid auctions, we analyzed a particular type of online auctions called lowest (highest) unique bid auctions. The data regarding the auctions analyzed in the paper can be downloaded here.

This material can be cited as

Rationality, irrationality and escalating behavior in lowest unique bid auctions

**F. Radicchi**, A. Baronchelli and L. A. N. Amaral

PloS ONE **7**, e29910 (2012) [pdf]

In the paper Rescaling citations of publications in Physics, we performed a statistical analysis of the citation patterns of papers published in journals of the American Physical Society. The tables summarizing the results of our analysis can be downloaded here.

This material can be cited as

Rescaling citations of publications in Physics

**F. Radicchi** and C.Castellano

Phys. Rev. E **83**, 046116 (2011) [pdf]

In the paper Finding statistically significant communities in networks, we proposed a novel community detection algorithm. The method is based on the statistical significance of clusters and the theory is developed with the mathematical tools of Order Statistics. The code of the algorithm can be downloaded here. Instructions on how to use the code are included in the package.

This software can be cited as

Finding statistically significant communities in networks

A. Lancichinetti, **F. Radicchi**, J.J. Ramasco and S.Fortunato

PloS ONE **6**, e18961 (2011) [pdf]

In the paper Information filtering in complex weighted networks, we proposed a technique (GloSS) for the computation of the statistical significance of edges in weighted networks. The code of the implementation of the method for the case of weights with real values can be downloaded here. The version of the program for networks with discrete weights can be download here. Instructions on how to compile the code and use the program are included in the packages. We have also produced some videos about the application of the filtering technique to the US airport network ( avi , mov ) and UK commuting network ( avi , mov ).

This material can be cited as

Information filtering in complex weighted networks

**F. Radicchi**, J.J. Ramasco and S. Fortunato

Phys. Rev. E **83**, 046101 (2011) [pdf]

This software allows to calculate the statistical significance of communities in networks. You may download the source code here (compilation requires GSL).

This software can be cited as

Statistical significance of communities in networks

A. Lancichinetti, **F. Radicchi** and J.J. Ramasco

Phys. Rev. E **81**, 046110 (2010) [pdf]

Phys Author Rank Algorithm is a website where physicists can check the evolution of their own scientific rank. Scientific rank is calculated using the Science Author Rank Algorithm on a weighted author citation network.

This ranking procedure can
be cited as

Diffusion of scientific credits and the ranking of scientists

F. Radicchi, S. Fortunato, B. Markines and A. Vespignani

Phys. Rev. E **80**, 056103 (2009) [pdf]

Activity: This file has 733335 lines. Each of them corresponds to a user and contains a variable number of columns. The first column reports the number of pairs between consecutive messages sent by the user. Then for each of these pairs is reported the time difference (resolution in hours). The last column contains the sum of all inter-event periods of time. The size of the file is 121Mb (384Mb unzipped).

Replies: This file contains 6511710 pairs message/reply. Each line contains three columns: the first two columns report the ids of the users, while the third contains the time difference (resolution in hours) between message and reply. The size of the file is 32Mb (105Mb unzipped).

These data can be cited as

Human activity in the web

F. Radicchi

Phys. Rev. E **80**, 026118 (2009) [pdf]

In the paper Benchmark graphs for testing community detection algorithms, we introduced a new class of artificial networks that pose a far harder test to community detection algorithms. The new benchmark is an extension of the benchmark by Girvan and Newman. In the latter, the nodes have the same degree and the communities have equal size. Here, the distributions of nodes’ degree and community size are power laws, with tunable exponents. The code to build the new benchmark graphs can be downloaded here. Instructions on how to use the code are included in the package.

This software can be cited as

Benchmark graphs for testing community detection algorithms

A. Lancichinetti, S. Fortunato and F. Radicchi

Phys. Rev. E **78**, 046110 (2008) [pdf]

In the paper Defining and identifying communities in networks, we proposed a community detection algorithm based on the edge clustering coefficient. The code of the algorithm can be downloaded here. Instructions on how to use the code are included in the package.

This software can be cited as

Defining and identifying communities in networks

F. Radicchi, C. Castellano, F. Cecconi, V. Loreto and D. Parisi

Proc. Natl. Acad. Sci. USA **101**, 2658-2663 (2004) [pdf]