In the paper Consistency pays off in science, we proposed a new metric to evaluate the performance of scholars. A demonstration of the indicator can be found at here.
This material can be cited as
Consistency pays off in science
S. Erkol, S. Sikdar, F. Radicchi and S. Fortunato
Quantitative Science Studies 4, 491–500 (2023) [pdf]
In the paper Multiplex reconstruction with partial information, we proposed an algorithm to determine the flavor of edges in multiplex networks from partial observation of their ground-truth topology. Code and data used in the paper are availble here.
This material can be cited as
Multiplex reconstruction with partial information
D. Kaiser, S. Patwardhan and F. Radicchi
Phys. Rev. E 107, 024309 (2023) [pdf]
In the paper Influence maximization in Boolean networks, we developed a method for the identification of minimal sets of nodes able to drive the dynamics of Boolean networks toward desired fixed points. Code and data used in the paper are availble here.
This material can be cited as
Influence maximization in Boolean networks
T. Parmer, L.M. Rocha and F. Radicchi
Nat. Commun. 13, 3457 (2022) [pdf]
In the paper Universality, criticality and complexity of information propagation in social media, we study information avalanches in social media. Code and data used in the paper are availble here and here.
This material can be cited as
Universality, criticality and complexity of information propagation in social media
D. Notarmuzi, C. Castellano, A. Flammini, D. Mazzilli and F. Radicchi
Nat. Commun. 13, 1308 (2022) [pdf]
In the paper Systematic comparison of graph embedding methods in practical tasks, we propose a standardized benchmark to assess the performance of arbitrary embedding methods in practical tasks. The benchmark is available here.
This material can be cited as
Systematic comparison of graph embedding methods in practical tasks
Y.J. Zhang, K.C. Yang and F. Radicchi
Phys. Rev. E 104, 044315 (2021) [pdf]
CoachScore uses publicly available data about soccer/basketball games to generate a weighted and directed network of contacts among coaches, and then measure their performance with CoachScore, a variant of the well-known PageRank centrality.
This ranking procedure can be cited as
Who is the best coach of all time? A network-based assessment of the career performance of professional sports coaches
Ş. Erkol and F. Radicchi
J. Complex Netw. 9, cnab012 (2021)
In the paper Principled approach to the selection of the 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
Principled approach to the selection of the embedding dimension of networks
W. Gu, A. Tandon, Y.Y. Ahn and F. Radicchi
Nat. Commun. 12, 3772 (2021) [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. Muñoz 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]
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.
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]