Santo Fortunato
Professor
Contact information
Email: santo.fortunato (at) aalto.fi, santo.fortunato (at) gmail.com
Mobile: +358 50 460 5511
Room: 3rd floor, F351
Visiting address:
F Building, Rakentajanaukio 2 C, 02150 Espoo
Postal address:
Dept. of Biomedical Engineering and Computational Science,
P.O.Box 12200, FI00076 Aalto, Finland
 Current
 About me
 Research
 Publications
 Teaching
 Citations
 Presentations
 In the press
 Collaborators
 Software
 Useful links
News
 The special issue Statistical Mechanics and Social Sciences, edited by Michael Macy, Sidney Redner and myself, has just been published in the Journal of Statistical Physics. You can find the contents here (Volume I, Volume II). Read our editorial for a brief overview.
 My historicalphilosophical introduction to the concept of social atom, first class of my course Mathematical modeling of social dynamics, can be seen here
 My keynote talk Community detection in networks opened the European Conference on Complex Systems 2012 (ECCS'12), Brussels (September the 3rd, 2012)
 I have been renominated a Distinguished Referee of the journal Europhysics Letters for my referee work in 2011. For more information, please see BECS news.
 On November the 16th, 2011, I have given my Installation Lecture at Aalto University, a short pedagogical presentation of the history and principles of sociophysics. The video of the lecture can be seen here.
 I am the winner of the Young Scientist Award for Socio and Econophysics 2011. The award ceremony was held at the annual meeting of the German Physical Society in Dresden, on March 14th, 2011 (photos: 1, 2, 3, 4). Following the reception of the award, two articles on myself and my work appeared in the newspaper Italia Oggi and in the popular magazine L'Espresso.
Recent papers
 In the commentary The case for caution in predicting scientists' future impact [Physics Today 66 (4), 89, 2013], we warn against the indiscriminate use of quantitative indicators of scientific impact, particularly the hindex, to evaluate academic careers, especially for young scientists.
 The paper Universality in voting behavior: an empirical analysis (Scientific Reports 3, 1049, 2013) shows that proportional elections with open lists lead to the same pattern for the distribution of performance of candidates across countries and years. Deviations from the general pattern are associated to specific differences in the election rules.
 In the paper World citation and collaboration networks: uncovering the role of geography in science (Scientific Reports 2, 902, 2012) we study the streams of citations and collaborations between different geographic locations, finding that they obey simple gravity laws.
 Take a look at our recent paper on the physics of elections: Physics peeks into the ballot box, Physics Today 65 (10), 7475, 2012.
 In the paper Characterizing and modeling citation dynamics (PLoS One 6, e24926, 2011) we find that citation distributions of networks of papers of the Americal Physical Society are described by shifted power laws and that citation dynamics is characterized by bursts in the early life of papers. Both features can be accounted for by a modified version of linear preferential attachment, where the paper attractiveness is heterogeneously distributed and decays with time.
 How do Nobel Prize Laureates accrue their scientific reputation? In the paper How citation boosts promote scientific paradigm shifts and Nobel Prizes (PLoS One 6, e18975, 2011) we show that groundbreaking discoveries make previous publications of the author visible and cited, even in different topics. This "authority effect" is measurable and can be used to distinguish outstanding scholars from normal ones. See the feature by Philip Ball on Nature News!
 In the paper Finding statistically significant communities in networks (PLoS One 6, e18961, 2011) we present OSLOM, the first multipurpose method to find communities in graphs, accounting for edge directions, edge weights, overlapping communities, hierarchies and community dynamics.
 What are the principles behind the dynamics of online popularity? In the paper Characterizing and modeling the dynamics of online popularity (Phys. Rev. Lett. 105, 158701, 2010) we show that popularity in online media does not change smoothly, but it experiences wild fluctuations, following power law distributions, due to exogenous factors.
 What do communities in real networks look like? In the paper Characterizing the community structure of complex networks (PLoS One 5, e11976, 2010) we have made a systematic analysis of various networked datasets, finding that communities can be categorized in classes according to their statistical properties and that each class comprises networks of the same (or similar) origin (communication, information, biological, social and technological networks).
 What is the best algorithm to find communities in networks? In the paper Community detection algorithms: a comparative analysis (Phys. Rev. E 80, 056117, 2009) we have tested the performances of several graph clustering methods on the benchmark graphs we have recently introduced. As a result, the Infomap method by Rosvall and Bergstrom appears to be the most reliable and should be adopted as a first approach, especially when one has no specific information on the network at study.
 The Website physauthorsrank.org is a portal that computes rankings between physicists, based on the SARA score, a measure of credit defined through the network of citations between scientific authors. The SARA score is described in detail in the paper Diffusion of scientific credits and the ranking of scientists, Physical Review E 80, 056103 (2009). Featured by Physics!
 In the paper Explosive percolation in scalefree networks (Phys. Rev. Lett. 103, 168701, 2009) we study scalefree networks built with a special process introduced by Achlioptas et al., in which links are placed such to slow down the formation of large clusters. We find that the percolation transition leading to the formation of the giant component displays analytical features at the threshold for any value of the degree exponent λ. For λ>3 the order parameter displays the trivial scaling expected for discontinuous transitions. The percolation threshold is finite for λ>~2.2, in contrast to standard random percolation. In the paper Explosive percolation: A numerical analysis (Phys. Rev. E 81, 036110, 2010) we present a detailed numerical analysis of the process including lattices, random graphs and scalefree networks.
 The paper Community detection in graphs (Phys. Rep. 486, 75174, 2010) is the first comprehensive review article on the problem of graph clustering, which consists in identifying clusters of vertices with a high internal density of edges, whereas the density of edges between clusters is comparatively low.
I was born in Augusta (Italy), on May the 31st, 1971. I carried out my undergraduate studies at the Department of Physics and Astronomy of the University of Catania (Italy), where I got my degree on July the 19th, 1995, with the mark of 110/110 cum laude, presenting the thesis "TwoParticleOneHole Excitations in the Continuum"
From September 1995 till June 1997 I worked at the Royal Institute of Technology (KTH) of Stockholm, Sweden, supported by a fellowship granted by the Swedish Institute. In Stockholm I kept working on nuclear physics, and specifically on issues related to my thesis work.
In March 1998 I began my PhD studies in the group of Theoretical High Energy Physics at the University of Bielefeld (Germany), which is renowned for its activity in lattice gauge theories. There I developed a theoretical framework that maps the deconfinement transition of SU(2) lattice gauge theory to the percolation transition of special clusters of nearest neighboring likesigned spin variables. I earned my PhD degree on November the 27th, 2000, with the mark of summa cum laude. The title of my PhD thesis was "Percolation and Deconfinement in SU(2) Gauge Theory".
I stayed in Bielefeld as a postdoctoral research associate until December 2004. As a postdoc I basically worked on percolation theory and its possible applications to lattice gauge theories and phenomenology of heavy ion collisions. In summer 2003 I got to know about complex networks at a seminar of my friend and colleague Vito Latora. After some reading, I was fascinated by the new topic, and in general by complex systems, so I decided to join the rapidly increasing scientific community that was dealing with this new research area.
From February 2005 to January 2007 I was postdoctoral research associate at the School of Informatics of Indiana University in Bloomington (Indiana, USA), working in the Complex Systems Group led by Prof. Alessandro Vespignani. During my stay at IU, I worked on several problems, from general theory of complex networks to the study of information and biological networks, to social dynamics.
From February 2007 till September 2009 I was research scientist at the Complex Networks Lagrange Laboratory of the Institute for Scientific Interchange (ISI) in Turin, Italy. Since October 2009 I am research leader at ISI.
In October 2011 I became Associate Professor of Complex Systems of the Department of Biomedical Engineering and Computational Science (BECS) of the School of Science of Aalto University in Espoo, Finland. Since March the 1st, 2014, I am Professor of Complex Systems at BECS.
My research is focused on the area of complex systems. A system is complex when its global properties cannot be simply inferred by extrapolation from the properties of its constituents. The interactions of the constituents are usually simple, but the heterogeneity of the interaction patterns, the presence of nonlinearity and feedback effects give rise to the emergence of global properties/phenomena, involving both the structure and the dynamics of the system. Such emergent properties were not originally designed or imposed to the system from outside, but are a genuine product of selforganization. Examples of complex systems are fractals (see figure), chaotic systems, animal and human societies, the World Wide Web, etc.
The famous Mandelbrot fractal, studied by Benoit Mandelbrot in 1979. Like all fractals, it is a selfsimilar geometric object, in which each part, however small, looks like the whole.
The field of complex systems is, by its very nature, interdisciplinary. Complex system scientists include physicists, mathematicians, computer scientists, biologists and engineers, with frequent collaborations between scholars with different backgrounds. I am actively working on complex networks theory and applications, and on statistical physics modelling of social dynamics.
Complex networks
A complex network is a graph endowed with the following basic properties:
 The distribution of the number of adjacent links to a node (degree) is broad, with a tail that often follows a power law
 The diameter, i.e. the longest of the shortest paths between nodes, is fairly small, growing only logarithmically with the size of the network (smallworld phenomenon)
 The clustering coefficient, i.e. the fraction of closed triads centered at a node, is significantly higher than in a random network with the same number of nodes and links
The field started in 1998 with the seminal paper of Duncan Watts and Steven Strogatz, in which they showed that several networks existing in nature, society and technology, have the smallworld property. The interest in this research was boosted by the empirical discoveries of the group of AlbertLászló Barabási, revealing that most real networks have a degree distribution with a power law tail. In this way, there is no characteristic value for the degree of a node, that is why people call these graphs scalefree networks.
In the last years, scholars have studied the properties of these networks, proposed models to explain their genesis and evolution, investigated how dynamical processes develop on these special graphs. For an account of the activity in this field we refer to the recent review by Boccaletti et al.
I am especially interested in the problem of the identification of community structure in networks. Real networks display a structure characterized by groups of nodes, called communities or modules, such that nodes of each group share more connections with the other nodes of the group than with the rest of the network. Detecting communities can lead to the discovery of functional units of biological networks, like proteinprotein interaction networks or metabolic networks, and disclose unknown properties of nodes. But the problem is hard and still open, in spite of the many approaches which have been suggested over the years. The main difficulty is that it is an illdefined problem, where there is still confusion about the basic elements, from the definition of community to the evaluation of partitions. Further complications are represented by the fact that nodes can belong to different groups (overlapping communities) and that groups may in turn represent subunits of larger modules (hierarchical structure).
I also study the structure and function of information networks, particularly of the web graph, i.e. the graph where the nodes represent documents of the World Wide Web and the links are the hyperlinks that allow to jump from one web page to another. In particular I investigate the interplay between the web graph structure, search engines and user behavior.
Statistical physics of social dynamics
Society is complex. Social interactions usually involve few individuals, yet nontrivial global phenomena can emerge. For instance, a consensus on some issue can be reached after many discussions between pairs of individuals or within small groups, even if the whole community is large. Similar dynamics can explain how people end up to share a common culture, or language. The global organization of the system can be achieved via simple local interactions between people, just like phase transitions are originated by elementary interactions between neighboring particles/spins. This parallelism is the motivation of the countless applications of statistical physics tools and models to describe large scale social phenomena, like opinion formation, cultural dissemination, language origin and evolution, emergence of hierarchies from initially egalitarian societies, etc.
My main goal is to lay solid foundations to the field, by characterizing large scale social phenomena by means of quantitative regularities. Only in this way it is possible to attempt a quantitative description of social phenomena, which is the best contribution that physicists could give. This can be accomplished by collecting and analyzing data referring to mass phenomena, like elections, marketing, etc. Some recent striking results on elections and voting behavior can be found here. Another possible avenue is to design controlled social experiments by means of the World Wide Web.
 Edited books
 Complex Networks, Proceedings of CompleNet 2009, International Workshop on Complex Networks, held in Catania, Italy, on May 2627, 2009. The editors are Santo Fortunato, Ronaldo Menezes, Giuseppe Mangioni and Vincenzo Nicosia. The volume belongs to Springer's series Studies in Computational Intelligence.
 Book chapters

Santo Fortunato, Claudio Castellano
Community structure in graphs
chapter of Springer's Encyclopedia of Complexity and System Science (2008),
Eprint arXiv:0712.2716 
Santo Fortunato, Dietrich Stauffer
Computer Simulations of Opinions and Their Reactions to Extreme Events
in Extreme Events in Nature and Society, S. Albeverio, V. Jentsch, H. Kantz editors, Springer, BerlinHeidelberg (2006)

Santo Fortunato, Claudio Castellano
 Reviews

Santo Fortunato
Community detection in graphs, Eprint arXiv: 0906.0612
Physics Reports 486, 75174 (2010) 
Claudio Castellano, Santo Fortunato, Vittorio Loreto
Statistical physics of social dynamics, Eprint arXiv: 0710.3256
Reviews of Modern Physics 81, 591646 (2009).

Santo Fortunato
 Journals

R. Kumar Pan, K. Kaski, S. Fortunato
World citation and collaboration network: uncovering the role of geography in science
Scientific Reports 2, 902 (2012) 
A. Lancichinetti, S. Fortunato
Consensus clustering in complex networks
Scientific Reports 2, 336 (2012) 
A. Lancichinetti, S. Fortunato
Limits of modularity maximization in community detection
Physical Review E 84, 066122 (2011) 
Y.H. Eom, S. Fortunato
Characterizing and modeling citation dynamics
PLoS One 6 (9), e24926 (2011) 
A. Lancichinetti, F. Radicchi,J. J. Ramasco, S. Fortunato
Finding statistically significant communities in networks
PLoS One 6 (4), e18961 (2011) 
F. Radicchi, J. J. Ramasco, S. Fortunato
Information filtering in complex weighted networks
Physical Review E 83, 046101 (2011) 
A. Lancichinetti, M. Kivela, J. Saramaki, S. Fortunato
Characterizing the community structure of complex networks
PLoS One 5(8), e11976 (2010) 
F. Radicchi, S. Fortunato
Explosive percolation: a numerical analysis
Physical Review E 81, 036110 (2010). 
A. Lancichinetti, S. Fortunato
Community detection algorithms: a comparative analysis
Physical Review E 80, 056117 (2009) 
F. Radicchi, S. Fortunato, B. Markines, A. Vespignani
Diffusion of scientific credits and the ranking of scientists
Physical Review E 80, 056103 (2009) 
F. Radicchi, S. Fortunato
Explosive percolation in scalefree networks
Physical Review Letters 103, 168701 (2009) 
S. Mandrà, S. Fortunato, C. Castellano
Coevolution of Glauberlike Ising dynamics and topology
Physical Review E 80, 056105 (2009) 
S. Fortunato, M. Boguñá, A. Flammini, F. Menczer
On local estimations of PageRank: a mean field approach
Internet Mathematics 4, 245266 (2009) 
A. Lancichinetti, S. Fortunato
Benchmark for testing community detection algorithms on directed and weighted graphs with overlapping communities
Physical Review E 80, 016118 (2009) 
A. Lancichinetti, S. Fortunato, J. Kertész
Detecting the overlapping and hierarchical community structure in complex networks
New Journal of Physics 11, 033015 (2009) 
F. Radicchi, A. Barrat, S. Fortunato, J. J. Ramasco
Renormalization flows in complex networks
Physical Review E 79, 026104 (2009) 
A. Lancichinetti, S. Fortunato, F. Radicchi
Benchmark graphs for testing community detection algorithms
Physical Review E 78, 046110 (2008) 
N. Perra, S. Fortunato
Spectral centrality measures in complex networks
Physical Review E 78, 036107 (2008) 
F. Radicchi, J. J. Ramasco, A. Barrat, S. Fortunato
Complex networks renormalization: flows and fixed points
Physical Review Letters 101, 148701 (2008) 
A. Lancichinetti, S. Fortunato, J. Kertész
Detecting the overlapping and hierarchical community structure of complex networks
New Journal of Physics 11, 033015 (2009) 
A. Arenas, A. Fernández, S. Fortunato, S. Gómez
Motifbased communities in complex networks
Journal of Physics A 41, 224001 (2008) 
S. Fortunato, M. Barthélemy
Resolution limit in community detection
Proceedings of the National Academy of Science of the USA 104 (1), 3641 (2007) 
S. Schnell, S. Fortunato, S. Roy
Is the intrinsic disorder of proteins the cause of the scalefree architecture of proteinprotein interaction networks?
Proteomics 7 (6), 961964 (2007) 
S. Fortunato, A. Flammini
Random walks on directed networks: the case of PageRank
Invited contribution published in the Special Issue on "Complex Networks' Structure and Dynamics"
International Journal of Bifurcation and Chaos 17 (7), 23432353 (2007) 
S. Fortunato, A. Flammini, F. Menczer
Scalefree network growth by ranking
Physical Review Letters 96, 218701 (2006) 
M. Ángeles Serrano, M. Boguñá, A. G. Maguitman, S. Fortunato, A. Vespignani
Decoding the structure of the WWW: a comparative analysis of Web crawls
ACM Transactions on the Web 1 (2), paper 10 (2007) 
S. Fortunato, M. Boguñá, A. Flammini, F. Menczer
Approximating PageRank from indegree
Lecture Notes in Computer Science 4936, 5971 (2008) 
S. Fortunato, A. Flammini, F. Menczer, A.Vespignani
Topical interests and the mitigation of search engine bias
Proceedings of the National Academy of Science of the USA 103(34), 1268412689 (2006) 
P. Blanchard, S. Fortunato, T. Krüger
Importance of extremists for the structure of social networks
Physical Review E 71, 056114 (2005) 
S. Fortunato, V. Latora, M. Marchiori
Method to find community structures based on information centrality
Physical Review E 70, 056104 (2004) 
S. Fortunato, M. Macy, S. Redner
Editorial: Statistical Mechanics and Social Sciences, special issue of Journal of Statistical Physics
Journal of Statistical Physics 151 (1), 18 (2013) 
A. Chatterjee, M. Mitrovic, S. Fortunato
Universality in voting behavior: an empirical analysis
Scientific Reports 3, 1049 (2013) 
O. Penner, R. Kumar Pan, A. M. Petersen, S. Fortunato
The case for caution in predicting scientists' future impact
Physics Today 66 (4), 89 (2013) 
A. M. Petersen, S. Fortunato, R. Kumar Pan, K. Kaski, O. Penner, M. Riccaboni, H. E. Stanley, F. Pammolli
Reputation and impact in academic careers
Eprint arXiv: 1303.7274 
S. Fortunato, C. Castellano
Physics peeks into the ballot box
Physics Today 65 (10), 7475 (2012) 
A. Mazloumian, Y.H. Eom, D. Helbing, S. Lozano, S. Fortunato
How citation boosts promote scientific paradigm shifts and Nobel Prizes
PLoS One 6 (5), e18975 (2011) 
J. Ratkiewicz, S. Fortunato, A. Flammini, F. Menczer, A. Vespignani
Characterizing and modeling the dynamics of online popularity
Physical Review Letters 105, 158701 (2010). 
F. Radicchi, S. Fortunato, C. Castellano
Universality of citation distributions: toward an objective measure of scientific impact
Proceedings of the National Academy of Science of the USA 105, 1726817272 (2008). 
S. Fortunato, C. Castellano
Scaling and universality in proportional elections
Physical Review Letters 99, 138701 (2007) 
S. Fortunato, V. Latora, A. Pluchino, A. Rapisarda
Vector opinion dynamics in a bounded confidence consensus model
International Journal of Modern Physics C 16 (10), 15351551 (2005) 
S. Fortunato
On the consensus threshold for the opinion dynamics of KrauseHegselmann
International Journal of Modern Physics C 16 (2), 259270 (2005) 
S. Fortunato
The Sznajd consensus model with continuous opinions
International Journal of Modern Physics C 16 (1), 1724 (2005) 
S. Fortunato
Universality of the threshold for complete consensus for the opinion dynamics of Deffuant et al.
International Journal of Modern Physics C 15 (9), 13011307 (2004) 
S. Fortunato
Damage spreading and opinion dynamics on scalefree networks
Physica A 348, 683 (2005) 
S. Fortunato
The KrauseHegselmann consensus model with discrete opinions
International Journal of Modern Physics C 15 (7), 10211029 (2004) 
S. Fortunato, A. Aharony, A. Coniglio, D. Stauffer
Number of spanning clusters at the highdimensional percolation thresholds
Physical Review E 70, 056116 (2004) 
S. Fortunato, D. Stauffer, A. Coniglio
Percolation in high dimensions is not understood
Physica A 334 (34), 307311 (2004) 
S. Fortunato
Cluster percolation and critical behaviour in spin models and SU(N) gauge theories
Journal of Physics A 36, 4269 (2003) 
S. Fortunato
Critical droplets and phase transitions in two dimensions
Physical Review B 67, 014102 (2003) 
S. Fortunato
Site percolation and phase transitions in two dimensions
Physical Review B 66, 054107 (2002) 
S. Fortunato, H. Satz
Cluster percolation and first order phase transitions in the Potts model
Nuclear Physics B 623, 493 (2002) 
S. Fortunato, H. Satz
Cluster percolation and pseudocritical behaviour in spin models
Physics Letters B 509, 189 (2001) 
P. Blanchard, S. Digal, S. Fortunato, D. Gandolfo, T. Mendes, H. Satz
Cluster percolation in O(n) spin models
Journal of Physics A 33, 860 (2000) 
S. Fortunato, H. Satz
Percolation and magnetization for generalized continuous spin models
Nuclear Physics B 598, 601 (2001) 
P. Bialas, P. Blanchard, S. Fortunato, D. Gandolfo, H. Satz
Percolation and magnetization in the continuous spin Ising model
Nuclear Physics B 583, 368 (2000) 
P. Blanchard, S. Fortunato, H. Satz
The Hagedorn temperature and partition thermodynamics
European Physical Journal C 34, 361366 (2004) 
S. Digal, S. Fortunato, H. Satz
Predictions for J/Ψ suppression by parton percolation
European Physical Journal C 32, 547553 (2004) 
S. Digal, S. Fortunato, P. Petreczky
Heavy quark free energies and screening in SU(2) gauge theory
Physical Review D 68, 034008 (2003) 
S. Digal, S. Fortunato, P. Petreczky, H. Satz
Parton percolation and J/Ψ suppression
Physics Letters B 549, 101 (2002) 
P. Blanchard, S. Fortunato, D. Gandolfo
Euler Poincaré characteristic and phase transition in the Potts model on Z²
Nuclear Physics B 644, 495 (2002) 
S. Fortunato, F. Karsch, P. Petreczky, H. Satz
Effective Z(2) spin models of deconfinement and percolation in SU(2) gauge theory
Physics Letters B 502, 321 (2001) 
S. Fortunato, H. Satz
Polyakov loop percolation and deconfinement in SU(2) gauge theory
Physics Letters B 475, 311 (2000) 
S. Fortunato, A. Insolia, R. J. Liotta, T. Vertse
Twoparticleonehole excitations in the continuum
Physical Review C 54, 3279 (1996)
Complex networks
Social dynamics
Percolation
Statistical field theory
Nuclear theory

R. Kumar Pan, K. Kaski, S. Fortunato
 Conference proceedings

J. Ratkiewicz, S. Fortunato, A. Flammini, F. Menczer, A. Vespignani
Traffic in Social Media II: Modeling Bursty popularity
Proceedings of SocialCom 2010, Symposium on Social Intelligence and Networking (SIN10). Paper ID SIN246. 
M.R. Meiss, F. Menczer, S. Fortunato, A. Flammini, A. Vespignani
Ranking Web Sites with Real User Traffic
Proceedings of the 2008 International Conference on Web Search and Data Mining (WSDM), Palo Alto, CA, USA, February 1112, 6575. 
S. Fortunato
Quality Functions in Community Detection
Proceedings of SPIE International Conference "Fluctuations and Noise 2007", La Pietra Conference Center, Florence (Italy), 2024 May 2007. 
S. Fortunato
Monte Carlo Simulations of Opinion Dynamics
Proceedings of the International Conference "Complexity, Metastability and Nonextensivity", Ettore Majorana Foundation and Center for Scientific Culture Erice (Sicily) 2026 July 2004. 
G. Andronico, A. Coniglio, S. Fortunato
A Geometrical Interpretation of Hyperscaling Breaking in the Ising Model
Proceedings of the XX International Symposium on Lattice Field Theory (LATTICE 2002), Boston, Massachusetts. Nuclear Physics B Proceedings Supplement 119, 876 (2003). 
S. Digal, S. Fortunato, P. Petreczky
Heavy Quark Free Energies and Screening in SU(2) Gauge Theory
Proceedings of the International Workshop "Strong and Electroweak Matter 2002", Heidelberg (Germany), World Scientific, Singapore. 
S. Fortunato
Sequential Quarkonium Suppression
Proceedings of the International Workshop "Quark Gluon Plasma and Heavy Ion Collisions", Laboratori Nazionali INFN, Frascati, Italy. Published in "Quark Gluon Plasma and Heavy Ion Collisions", World Scientific (2002). 
S. Fortunato, H. Satz
Cluster Percolation and Pseudocritical Behaviour in Spin Models
Proceedings of the XIX International Symposium on Lattice Field Theory (LATTICE 2001), Berlin (Germany). Nuclear Physics B Proceedings Supplement 106, 890 (2002). 
S. Fortunato, F. Karsch, P. Petreczky, H. Satz
Percolation and Critical Behaviour in SU(2) Gauge Theory
Proceedings of the XVIII International Symposium on Lattice Field Theory (LATTICE 2000), Bangalore (India). Nuclear Physics B Proceedings Supplement 94, 398 (2001). 
S. Fortunato, H. Satz
Percolation and Deconfinement in SU(2) Gauge Theory
Proceedings of the 3rd Catania Relativistic Ion Studies (CRIS 2000) Acicastello (Italy). Nuclear Physics A 681, 466 (2001). 
S. Fortunato, H. Satz
Percolation and Deconfinement in SU(2) Gauge Theory
Proceedings of the XVII International Symposium on Lattice Field Theory (LATTICE '99), Pisa (Italy). Nuclear Physics B Proceedings Supplement 83, 452 (2000).

J. Ratkiewicz, S. Fortunato, A. Flammini, F. Menczer, A. Vespignani
 Other publications

S. Fortunato, R. Cohen, S. Havlin
Complex Networks
Journal of Statistical Physics 142, 640641 (2011) 
F. Menczer, S. Fortunato, A. Flammini, A. Vespignani
Googlearchy or Googlocracy?
Invited article on IEEE Spectrum online, February 2006 
S. Fortunato
Percolation and Deconfinement in SU(2) Gauge Theory
PhD Thesis, University of Bielefeld (Germany), 2000. Advisor: Prof. H. Satz 
S. Fortunato
TwoParticleOneHole Excitations in the Continuum
University Thesis, University of Catania (Italy)

S. Fortunato, R. Cohen, S. Havlin
Teaching
Content will be added in the future.
Citation statistics of my papers:
 Thomson Reuters (ISI Web of Science)
 Google Scholar
Here are the slides of some talks I have given around the world.
 Introduction to complex networks, invited lecture at the International Conference and School NetSci 2011, Central European University, Budapest, Hungary (610 June, 2011)
 Phenomenology of social dynamics, invited talk at the International Workshop Social Data Mining and Knowledge Building, Institute of Pure and Applied Mathematics (IPAM), UCLA, Los Angeles, USA (59 November, 2007)
 Phenomenology of social dynamics, invited talk at the International Conference and School Statistical Physics of Social Dynamics: Opinions, Semiotic Dynamics, and Language, Ettore Majorana Foundation and Center for Scientific Culture, Erice, Italy (1419 July, 2007)
 Renormalization and hierarchy in complex networks, talk given at the International Conference Complex Networks: from Biology to Information Technology, Sardegna Ricerche, Pula, Italy (26 July, 2007)
 Quality functions in community detection, invited talk at SPIE International Conference Fluctuations & Noise 2007, La Pietra Conference Center, Florence, Italy (2024 May, 2007)
 Word of mouth and universal voting behavior in proportional elections, invited seminar for the Complex Adaptive Systems Group (CASG), Hilary Term 2007, Saïd Business School, University of Oxford, UK (27 February, 2007)
 Building Website popularity, invited talk at the International Conference Alife X, Bloomington, IN, USA (37 June, 2006)
 Scalefree network growth by ranking, talk given at the 2006 APS March Meeting, Baltimore, MD, USA (1317 March, 2006)
 Two articles on myself and my work, following the reception of the Young Scientist Award for Socio and Econophysics, have appeared in September and October 2011 in the Italian popular magazine L'Espresso and in the newspaper Italia Oggi. The piece on Italia Oggi can be read here. The one on L'Espresso here.
 The paper How citation boosts promote scientific paradigm shifts and Nobel Prizes was featured in Nature News.
 The paper Characterizing and modeling the dynamics of online popularity was featured in Physics and PhysOrg.com.
 The paper Diffusion of scientific credits and the ranking of scientists was featured by Physicsworld.
 The paper Universality of citation distributions: toward an objective measure of scientific impact was featured by Nature News, Nature Nanotechnology, Ansa, Yahoo Italia Notizie, Gazzetta del Sud and many blogs.
 The paper Complex networks renormalization: flows and fixed point was featured by Physics.
 A Radio Interview on the international workshop Sociophysics: status and perspectives (organized by me at ISI in May, 2008) was aired on Radio 3 (MP3 in Italian). An interview on Sociophysics has been published in Il Manifesto.
 The paper Scaling and universality in proportional elections was featured by New Scientist, Physicsworld, RomaOne.it, Galileo, il Manifesto. A live Radio interview was broadcast by Radio Capital (MP3 in Italian).

My PNAS paper on the egalitarian effect of search engines was featured by New Scientist, MIT Technology Review, IEEE CiSE, Scientific American MIND, New Scientist Online, UPI, VNUnet, Forskning & Framsteg (Sweden), Sole 24 Ore (Italy), Ars Technica and Slashdot. Interviews aired on BBC World Service (MP3), Deutschlandradio (MP3), WFHB (MP3), and WFIU.
An earlier version of the paper was cited by The Economist, Slashdot, PhysicsWeb, IDS, LeScienze (Italian Edition of Scientific American), and IEEE Spectrum Online (see also our invited article in IEEE Spectrum). Radio interviews were broadcast by Radio 3 (MP3 in Italian) and Swiss Radio (MP3 in Italian). Other news sources that picked up the story include Monsters and Critics, PhysOrg, TechNews Daily, Political Gateway, Daily India, ACM TechNews (Aug 9, Aug 28 2006), IT Week, Science Daily, EurekAlert, v3.co.uk, LaboratoryTalk, PC World, SDA Asia, What PC, BrightSurf, PC Authority, TRN, and hundreds of blogs.  The paper Scalefree network growth by ranking (PRL 96, 21870, 2006) was featured by ScienceDaily, PhysOrg and Personal Computer World.
 The paper Decoding the structure of the WWW: a comparative analysis of Web crawls (ACM Transactions on the Web 1, No. 2, paper 10, 2007) was featured by TRN.
Benchmark graphs to test community detection algorithms
Methods to detect communities in graphs need to be thoroughly tested. To do that, one needs benchmark graphs with a builtin community structure, that the methods should identify. Standard benchmarks, like that by Girvan and Newman, do not account for important features of real networks, like the fattailed distributions of node degree and community size. Therefore, we have proposed new classes of benchmark graphs, in which the distributions of node degree and community size are both power laws, with tunable exponents. In the paper Benchmark graphs for testing community detection algorithms, we have proposed a benchmark for the simplest case of undirected and unweighted graphs.
In a more recent paper, entitled Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities, we have extended our benchmark to account for the existence of overlapping communities, and to directed and weighted networks. Recently we have proposed also a hierarchical version of the benchmark, in which clusters are embedded in larger clusters.
 Package 1 includes the code to generate undirected and unweighted graphs with overlapping communities. The extent of the overlap can be tuned by input, and it can be set to zero if one is interested in nonoverlapping clusters.
 Package 2 includes the code to generate undirected weighted graphs with possibly overlapping communities.
 Package 3 includes the code to generate directed unweighted graphs with possibly overlapping communities.
 Package 4 includes the code to generate directed weighted graphs with possibly overlapping communities.
 Package 5 includes the code to generate the hierarchical benchmarks, with communities inside other communities.
The code is written in C++. In each package there is a Readme file where you can find instructions on how to use the code and simple examples.
Generalized normalized mutual information
In our paper Detecting the overlapping and hierarchical community structure in complex networks (New J. Phys. 11, 033015, 2009) we have introduced a measure of similarity between partitions that can be applied also to compare covers, i.e. divisions of a network into overlapping communities. The measure is based on the normalized mutual information used in information theory and regularly adopted by scholars to compare partitions of a network in communities. The standard normalized mutual information cannot be trivially extended to the case of overlapping communities. The code to compute our new measure for a pair of partitions/covers can be found here. The new measure does not coincide with the standard normalized mutual information when communities do not overlap, but it is quite close. For more information on the measure check this link.
Complex systems groups
 Complex Networks Collaboratory (CxNets)
 Complex Systems Group, Department of Informatics, Indiana University, Bloomington, IN, USA
 Northwestern Institute of Complex Systems (NICO), Northwestern University, Evanston, IL, USA
 New England Complex Systems Institute (NECSI)
 Center for the Study of Complex Systems, University of Michigan, Ann Arbor, MI, USA
 Santa Fe Institute (SFI)
 Center for Complex Network Research, University of Notre Dame, South Bend, IL, USA
 Complex Systems Barcelona, Barcelona, Spain
 Institute for CrossDisciplinary Physics and Complex Systems (IFISC), Universitat de les Illes Balears, Mallorca, Spain
Books
 S. Dorogovtsev, J. F. F. Mendes, Evolution of Networks: From Biological Nets to the Internet and WWW, Oxford University Press, 2003
 A.L. Barabási, Linked: The New Science of Networks, Perseus Books Group, 2002
 R. PastorSatorras, A. Vespignani, Evolution and Structure of the Internet: a Statistical Physics Approach, Cambridge University Press, 2004
 G. Caldarelli, ScaleFree Networks: Complex Webs in Nature and Technology, Oxford University Press, 2007
 R. Axelrod, Evolution of Cooperation, Basic Books, 1984
 R. Axelrod, The Complexity of Cooperation: AgentBased Models of Competition and Collaboration, Princeton University Press, 1997
 P. Ball, Critical Mass: How One Thing Leads to Another, Farrar, Straus and Giroux, 2004
 B. M. Roehner, Driving Forces in Physical, Biological and Socioeconomic Phenomena, Cambridge University Press, 2007
 E. Bonabeau, M. Dorigo, G. Theraulaz, Swarm Intelligence: From Natural to Artificial Systems, Oxford University Press, 1999
 S. Strogatz, Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry and Engineering, Perseus Books Group, 2001
Software
 NetworkWorkBench (NWB), a workbench for network scientists
 Pajek, a program for large network analysis