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    • Santo Fortunato
    • Santo Fortunato: Influence Statistics

      Santo Fortunato

      Santo Fortunato

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      Indiana University Network Science Institute (IUNI), Bloomington, Indiana, 47408, USA. | Luddy School of Informatics, Computing and Engineering, Indiana University, ...

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      Santo Fortunato:Expert Impact

      Concepts for whichSanto Fortunatohas direct influence:Complex networks,Community structure,Community detection,Spin models,Gauge theory,Consensus clustering,Cluster percolation,Real networks.

      Santo Fortunato:KOL impact

      Concepts related to the work of other authors for whichfor which Santo Fortunato has influence:Community detection,Complex networks,Social network,Overlapping communities.

      KOL Resume for Santo Fortunato

      Year
      2022

      Indiana University Network Science Institute (IUNI), Bloomington, Indiana, 47408, USA.

      2021

      Center for Complex Networks and Systems Research, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA

      2020

      School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana, United States of America

      Indiana University Network Science Institute, Indiana University, Bloomington, USA

      2019

      Indiana University Network Science Institute, Bloomington, IN, USA; School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, USA.

      2018

      Indiana University Network Science Institute, Indiana University, Bloomington, IN 47408, USA.

      Aalto University

      2017

      School of Informatics and Computing, Indiana University Bloomington, IN 47405, USA.

      Indiana University Network Science Institute, Bloomington, IN, USA

      2016

      Center for Complex Networks and Systems Research, School of Informatics and Computing, and Indiana University Network Science Institute (IUNI), Indiana University, Bloomington, USA

      Department of Computer Science, Aalto University, Finland.

      2015

      Department of Computer Science, Aalto University School of Science, P.O. Box 12200, 00076 Aalto, Finland.

      Complex Systems Unit, Aalto University School of Science, P.O. Box 12200, FI-00076, Finland

      2014

      Department of Biomedical Engineering and Computational Science, Aalto University School of Science, P. O. Box 12200, FI-00076, Finland.

      Aalto University, Finland

      2013

      Aalto University School of Science, Aalto, Finland

      2012

      Department of Biomedical Engineering and Computational Science, School of Science, Aalto University, P.O. Box 12200, FI-00076, Espoo, Finland

      Complex Networks and Systems Lagrange Lab, ISI Foundation, Via Alassio 11/C, 10126 Torino, Italy

      2011

      Complex Networks and System Lagrange Laboratory, ISI Foundation, Viale S. Severo 65, 10133, Torino, Italy

      Department of Biomedical Engineering and Computational Science, School of Science, Aalto University, 12200, FI-00076, Espoo, Finland

      2010

      Complex Networks and Systems Lagrange Lab, Institute for Scientific Interchange, Torino, Italy

      2009

      Complex Networks and Systems, Institute for Scientific Interchange (ISI), Viale S. Severo 65, 10133 Torino, Italy

      2008

      ISI Foundation, Torino, Italy

      School of Informatics, Indiana University, IN 47406, Bloomington, USA

      2007

      Complex Systems Group, Indiana University School of Informatics, Bloomington, IN, USA

      Fakultät für Physik, Universität Bielefeld, Bielefeld, Germany

      Indiana University and Institute for Scientific Interchange, Turin, Italy

      2006

      Complex Networks Lagrange Laboratory (CNLL), ISI Foundation, 10133 Torino, Italy; and

      Institut für Physik, Universität Bielefeld, Bielefeld, Germany

      School of Informatics, Indiana University, Bloomington, Indiana 47406, USA

      2005

      Dipartimento di Fisica e Astronomia and INFN sezione di Catania, Universitá di Catania, Catania I-95123, Italy

      School of Informatics, Indiana University, Bloomington, IN 47408, USA

      Fakultät für Physik, Universität Bielefeld, D-33501, Bielefeld, Germany

      2004

      Fakultät für Physik, Universität Bielefeld, Postfach 100 131, 33501, Bielefeld, Germany

      2003

      Fakulta¨t fu¨r Physik, Universita¨t Bielefeld, D-33615 Bielefeld, Germany

      2000

      Fakultät für Physik, Universität Bielefeld, D-33615, Bielefeld, Germany

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      Sample of concepts for which Santo Fortunato is among the top experts in the world.
      Concept World rank
      randomness real networks #1
      – hegselmann #1
      collective social behavior #1
      thermal exponents model #1
      features spin models #1
      clusters nearestneighbor spins #1
      cultural language dynamics #1
      nominal citation values #1
      science percolation transition #1
      fortuin–kasteleyn clusters #1
      decisionmaking processes humans #1
      gn modularity modularity #1
      real citation values #1
      work percolation behaviour #1
      dynamics computer models #1
      product rule systems #1
      fitness histogram #1
      nodes prestige values #1
      conjecture numerical evidence #1
      difficulties parallelisms #1
      statistics candidates performance #1
      voters atoms #1
      graph partitioningsocial science #1
      100000 usd #1
      achlioptas processes paper #1
      systems structure configurations #1
      theoretical politics #1
      map collective behavior #1
      physics papers capability #1
      sites bonds probability #1
      planted community structure #1
      specific sitebond clusters #1
      clusters thermal counterpart #1
      careers winning #1
      people xevents #1
      graphs subjectintroductionelements #1
      general problem methods #1
      fk clusters work #1
      renormalization flows #1
      proportional elections #1
      elite revisited #1
      model hamoltonian #1
      networks structural communities #1
      Sign-in to see all concepts, it's free!

      Prominent publications by Santo Fortunato

      KOL-Index: 8948

      Community structure is one of the most important features of real networks and reveals the internal organization of the nodes. Many algorithms have been proposed but the crucial issue of testing, i.e., the question of how good an algorithm is, with respect to others, is still open. Standard tests include the analysis of simple artificial graphs with a built-in community structure, that the algorithm has to recover. However, the special graphs adopted in actual tests have a structure that ...

      Known for Benchmark Graphs | Community Detection | Biological Models | Nodes Algorithms | Theoretical Neural Networks
      KOL-Index: 6778

      Many complex networks display a mesoscopic structure with groups of nodes sharing many links with the other nodes in their group and comparatively few with nodes of different groups. This feature is known as community structure and encodes precious information about the organization and the function of the nodes. Many algorithms have been proposed but it is not yet clear how they should be tested. Recently we have proposed a general class of undirected and unweighted benchmark graphs, ...

      Known for Community Detection Algorithms | Groups Nodes | Communities Feature | Modularity Optimization | Real Systems
      KOL-Index: 6687

      Community structure is one of the main structural features of networks, revealing both their internal organization and the similarity of their elementary units. Despite the large variety of methods proposed to detect communities in graphs, there is a big need for multi-purpose techniques, able to handle different types of datasets and the subtleties of community structure. In this paper we present OSLOM (Order Statistics Local Optimization Method), the first method capable to detect ...

      Known for Statistical Models | Networks Community Structure | Local Optimization | Fitness Function | Edge Weights
      KOL-Index: 6460

      Uncovering the community structure exhibited by real networks is a crucial step toward an understanding of complex systems that goes beyond the local organization of their constituents. Many algorithms have been proposed so far, but none of them has been subjected to strict tests to evaluate their performance. Most of the sporadic tests performed so far involved small networks with known community structure and/or artificial graphs with a simplified structure, which is very uncommon in ...

      Known for Biological Models | Community Detection | Sci Usa | Theoretical Neural Networks | Benchmark Graphs
      KOL-Index: 6361

      The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i.e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Such clusters, or communities, can be considered as fairly independent compartments of a graph, playing a ...

      Known for Vertices Clusters | Community Detection | Graphs Problem | Real Systems | Computer Science
      KOL-Index: 6184

      Most of the complex social, technological, and biological networks have a significant community structure. Therefore the community structure of complex networks has to be considered as a universal property, together with the much explored small-world and scale-free properties of these networks. Despite the large interest in characterizing the community structures of real networks, not enough attention has been devoted to the detection of universal mechanisms able to spontaneously ...

      Known for Triadic Closure | Complex Networks | Community Structure | Node Degree | Phase Transition
      KOL-Index: 6131

      BACKGROUND: Community structure is one of the key properties of complex networks and plays a crucial role in their topology and function. While an impressive amount of work has been done on the issue of community detection, very little attention has been so far devoted to the investigation of communities in real networks.

      METHODOLOGY/PRINCIPAL FINDINGS: We present a systematic empirical analysis of the statistical properties of communities in large information, communication, ...

      Known for Complex Networks | Community Structure | Key Properties | Methodology Principal Findings | Drosophila Melanogaster
      KOL-Index: 5918

      Reputation is an important social construct in science, which enables informed quality assessments of both publications and careers of scientists in the absence of complete systemic information. However, the relation between reputation and career growth of an individual remains poorly understood, despite recent proliferation of quantitative research evaluation methods. Here, we develop an original framework for measuring how a publication's citation rate Δc depends on the reputation of ...

      Known for Academic Careers | Citation Rate | Cumulative Advantage | Authors Reputation | Highly Cited
      KOL-Index: 5684

      Many networks in nature, society and technology are characterized by a mesoscopic level of organization, with groups of nodes forming tightly connected units, called communities or modules, that are only weakly linked to each other. Uncovering this community structure is one of the most important problems in the field of complex networks. Networks often show a hierarchical organization, with communities embedded within other communities; moreover, nodes can be shared between different ...

      Known for Community Structure | Hierarchical Organization Communities | Complex Networks Networks | Fitness Function | Groups Nodes
      KOL-Index: 5654

      Detecting community structure is fundamental for uncovering the links between structure and function in complex networks and for practical applications in many disciplines such as biology and sociology. A popular method now widely used relies on the optimization of a quantity called modularity, which is a quality index for a partition of a network into communities. We find that modularity optimization may fail to identify modules smaller than a scale which depends on the total size of ...

      Known for Modularity Optimization | Community Detection | Resolution Limit | Practical Applications | Biology Sociology
      KOL-Index: 5630

      We study the distributions of citations received by a single publication within several disciplines, spanning broad areas of science. We show that the probability that an article is cited c times has large variations between different disciplines, but all distributions are rescaled on a universal curve when the relative indicator c(f) = c/c(0) is considered, where c(0) is the average number of citations per article for the discipline. In addition we show that the same universal behavior ...

      Known for Citation Distributions | Scientific Impact | Objective Measure | Relative Indicator | Universal Behavior
      KOL-Index: 5169

      Major depressive disorder (MDD) is known to be associated with altered interactions between distributed brain regions. How these regional changes relate to the reorganization of cortical functional systems, and their modulation by antidepressant medication, is relatively unexplored. To identify changes in the community structure of cortical functional networks in MDD, we performed a multiscale community detection algorithm on resting-state functional connectivity networks of unmedicated ...

      Known for Community Structure | Image Processing | Major Depressive Disorder | Functional Systems | Cerebral Cortex
      KOL-Index: 5046

      The empirical validation of community detection methods is often based on available annotations on the nodes that serve as putative indicators of the large-scale network structure. Most often, the suitability of the annotations as topological descriptors itself is not assessed, and without this it is not possible to ultimately distinguish between actual shortcomings of the community detection algorithms, on one hand, and the incompleteness, inaccuracy, or structured nature of the data ...

      Known for Network Structure | Data Metadata | Principled Method | Community Detection | Generative Model
      KOL-Index: 4865

      Community structures are an important feature of many social, biological, and technological networks. Here we study a variation on the method for detecting such communities proposed by Girvan and Newman and based on the idea of using centrality measures to define the community boundaries [M. Girvan and M. E. J. Newman, Proc. Natl. Acad. Sci. U.S.A. 99, 7821 (2002)]. We develop an algorithm of hierarchical clustering that consists in finding and removing iteratively the edge with the ...

      Known for Community Structures | Girvan Newman | Methods Algorithm | Method Communities | Centrality Measures
      KOL-Index: 4790

      Complex networks are characterized by heterogeneous distributions of the degree of nodes, which produce a large diversification of the roles of the nodes within the network. Several centrality measures have been introduced to rank nodes based on their topological importance within a graph. Here we review and compare centrality measures based on spectral properties of graph matrices. We shall focus on PageRank (PR), eigenvector centrality (EV), and the hub and authority scores of the HITS ...

      Known for Centrality Measures | Complex Networks | Nodes Network | Spectral Properties | Pagerank Eigenvector

      Key People For Complex Networks

      Top KOLs in the world
      #1
      AlbertLászló Barabási
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      #2
      Réka Albert
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      #3
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      Indiana University Network Science Institute (IUNI), Bloomington, Indiana, 47408, USA. | Luddy School of Informatics, Computing and Engineering, Indiana University, Bloomington, Indiana 47408, USA. | Center for Complex Networks and Systems Research,

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