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, 26582663 (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  


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 online 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 goldmedal performances at the Olympic Games  
In the paper Universality, limits and predictability of goldmedal 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 goldmedal 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, 597602 (2015) [pdf] and Percolation in real multiplex networks G. Bianconi and F. Radicchi Phys. Rev. E 94, 060301(R) (2016) [pdf] 

Maximum entropy sampling on complex networks  
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] 