Resources

Edge clustering community detection algorithm
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].
Benchmark for community detection algorithms
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].
Ebay dataset
The dataset consists of two files
1 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).
2 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].
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]
Statistical significance of communities in networks
This software allows to calculate the statistical significance of communities in networks. You may download the source code (compilation requires GSL) or test the significance of your communities by simply using our on-line tool.
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]
Information filtering in complex weighted networks
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]
Order Statistics Local Optimization Method (OSLOM)
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]
Rescaling citations of publications in Physics
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]
Rationality, irrationality and escalating behavior in online auctions
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]

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

Google Scholar Citations data set
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]

Tennis Prestige
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]
Percolation in real interdependent networks
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
Nature Phys. 11, 597-602 (2015) [pdf]
and
Percolation in real multiplex networks
G. Bianconi and F. Radicchi
arXiv:1610.08708 (2016) [pdf]