adjacent possible air traffic complex network complexity complex_networks complex_systems creativity cuskley data_compression dynamical_systems evolutionary_dynamics gravino information_theory innovation_dynamics kreyon language_dynamics language_games local optimization loreto monechi opinion_dynamics phylogeny relevant_literature servedio social_dynamics statistical_physics techno_social_systems tria XTribe zippers
2012 |
Miguel, Maxi San; Johnson, Jeffrey; Kertesz, Janosz; Kaski, Kimmo; Diaz-Guilera, Albert; Mackay, Robert; Loreto, Vittorio; Erdi, Peter; Helbing, Dirk Challenges in complex systems science (Journal Article) THE EUROPEAN PHYSICAL JOURNAL. SPECIAL TOPICS, 214 , pp. 245–271, 2012. (Abstract | BibTeX | Tags: complex systems, loreto, social_dynamics) @article{b, title = {Challenges in complex systems science}, author = {Maxi San Miguel and Jeffrey H. Johnson and Janosz Kertesz and Kimmo Kaski and Albert Diaz-Guilera and Robert S. Mackay and Vittorio Loreto and Peter Erdi and Dirk Helbing}, year = {2012}, date = {2012-01-01}, journal = {THE EUROPEAN PHYSICAL JOURNAL. SPECIAL TOPICS}, volume = {214}, pages = {245--271}, publisher = {SPRINGER HEIDELBERG}, abstract = {FuturICT foundations are social science, complex systems science, and ICT. The main concerns and challenges in the science of complex systems in the context of FuturICT are laid out in this paper with special emphasis on the Complex Systems route to Social Sciences. This include complex systems having: many heterogeneous interacting parts; multiple scales; complicated transition laws; unexpected or unpredicted emergence; sensitive dependence on initial conditions; path-dependent dynamics; networked hierarchical connectivities; interaction of autonomous agents; self-organisation; non-equilibrium dynamics; combinatorial explosion; adaptivity to changing environments; co-evolving subsystems; ill-defined boundaries; and multilevel dynamics. In this context, science is seen as the process of abstracting the dynamics of systems from data. This presents many challenges including: data gathering by large-scale experiment, participatory sensing and social computation, managing huge distributed dynamic and heterogeneous databases; moving from data to dynamical models, going beyond correlations to cause-effect relationships, understanding the relationship between simple and comprehensive models with appropriate choices of variables, ensemble modeling and data assimilation, modeling systems of systems of systems with many levels between micro and macro; and formulating new approaches to prediction, forecasting, and risk, especially in systems that can reflect on and change their behaviour in response to predictions, and systems whose apparently predictable behaviour is disrupted by apparently unpredictable rare or extreme events. These challenges are part of the FuturICT agenda.}, keywords = {complex systems, loreto, social_dynamics}, pubstate = {published}, tppubtype = {article} } FuturICT foundations are social science, complex systems science, and ICT. The main concerns and challenges in the science of complex systems in the context of FuturICT are laid out in this paper with special emphasis on the Complex Systems route to Social Sciences. This include complex systems having: many heterogeneous interacting parts; multiple scales; complicated transition laws; unexpected or unpredicted emergence; sensitive dependence on initial conditions; path-dependent dynamics; networked hierarchical connectivities; interaction of autonomous agents; self-organisation; non-equilibrium dynamics; combinatorial explosion; adaptivity to changing environments; co-evolving subsystems; ill-defined boundaries; and multilevel dynamics. In this context, science is seen as the process of abstracting the dynamics of systems from data. This presents many challenges including: data gathering by large-scale experiment, participatory sensing and social computation, managing huge distributed dynamic and heterogeneous databases; moving from data to dynamical models, going beyond correlations to cause-effect relationships, understanding the relationship between simple and comprehensive models with appropriate choices of variables, ensemble modeling and data assimilation, modeling systems of systems of systems with many levels between micro and macro; and formulating new approaches to prediction, forecasting, and risk, especially in systems that can reflect on and change their behaviour in response to predictions, and systems whose apparently predictable behaviour is disrupted by apparently unpredictable rare or extreme events. These challenges are part of the FuturICT agenda. |
Conte, Rosaria; Gilbert, Nigel; Bonelli, Giulia; Cioffi-Revilla, Claudio; Deffuant, Guillaume; Kertesz, Janosz; Loreto, Vittorio; Moat, Susy; Nadal, Jean-Pierre; Sanchez, Ancho; Nowak, Andrzej; Flache, Andreas; Miguel, Maxi San; Helbing, Dirk Manifesto of computational social science (Journal Article) THE EUROPEAN PHYSICAL JOURNAL. SPECIAL TOPICS, 214 , pp. 325–346, 2012. (Abstract | BibTeX | Tags: loreto, social_dynamics) @article{b, title = {Manifesto of computational social science}, author = {Rosaria Conte and Nigel Gilbert and Giulia Bonelli and Claudio Cioffi-Revilla and Guillaume Deffuant and Janosz Kertesz and Vittorio Loreto and Susy Moat and Jean-Pierre Nadal and Ancho Sanchez and Andrzej Nowak and Andreas Flache and Maxi San Miguel and Dirk Helbing}, year = {2012}, date = {2012-01-01}, journal = {THE EUROPEAN PHYSICAL JOURNAL. SPECIAL TOPICS}, volume = {214}, pages = {325--346}, abstract = {The increasing integration of technology into our lives has created unprecedented volumes of data on society’s everyday behaviour. Such data opens up exciting new opportunities to work towards a quantitative understanding of our complex social systems, within the realms of a new discipline known as Computational Social Science. Against a background of financial crises, riots and international epidemics, the urgent need for a greater comprehension of the complexity of our interconnected global society and an ability to apply such insights in policy decisions is clear. This manifesto outlines the objectives of this new scientific direction, considering the challenges involved in it, and the extensive impact on science, technology and society that the success of this endeavour is likely to bring about.}, keywords = {loreto, social_dynamics}, pubstate = {published}, tppubtype = {article} } The increasing integration of technology into our lives has created unprecedented volumes of data on society’s everyday behaviour. Such data opens up exciting new opportunities to work towards a quantitative understanding of our complex social systems, within the realms of a new discipline known as Computational Social Science. Against a background of financial crises, riots and international epidemics, the urgent need for a greater comprehension of the complexity of our interconnected global society and an ability to apply such insights in policy decisions is clear. This manifesto outlines the objectives of this new scientific direction, considering the challenges involved in it, and the extensive impact on science, technology and society that the success of this endeavour is likely to bring about. |
2009 |
Castellano, Claudio; Fortunato, Santo; Loreto, Vittorio Statistical physics of social dynamics (Journal Article) REVIEWS OF MODERN PHYSICS, 81 , pp. 591–657, 2009. (Abstract | Links | BibTeX | Tags: loreto, social_dynamics) @article{b, title = {Statistical physics of social dynamics}, author = {Claudio Castellano and Santo Fortunato and Vittorio Loreto}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-65549108449&partnerID=65&md5=58a59b6faabadae96189e4b4164e6dbd http://samarcanda.phys.uniroma1.it/vittorioloreto/PAPERS/2009/RevModPhys_81_000591.pdf}, year = {2009}, date = {2009-01-01}, journal = {REVIEWS OF MODERN PHYSICS}, volume = {81}, pages = {591--657}, publisher = {American Institute of Physics:2 Huntington Quadrangle, Suite 1NO1:Melville, NY 11747:(800)344-6902, (631)576-2287, EMAIL: subs@aip.org, INTERNET: http://www.aip.org, Fax: (516)349-9704}, abstract = {Statistical physics has proven to be a fruitful framework to describe phenomena outside the realm of traditional physics. Recent years have witnessed an attempt by physicists to study collective phenomena emerging from the interactions of individuals as elementary units in social structures. A wide list of topics are reviewed ranging from opinion and cultural and language dynamics to crowd behavior, hierarchy formation, human dynamics, and social spreading. The connections between these problems and other, more traditional, topics of statistical physics are highlighted. Comparison of model results with empirical data from social systems are also emphasized.}, keywords = {loreto, social_dynamics}, pubstate = {published}, tppubtype = {article} } Statistical physics has proven to be a fruitful framework to describe phenomena outside the realm of traditional physics. Recent years have witnessed an attempt by physicists to study collective phenomena emerging from the interactions of individuals as elementary units in social structures. A wide list of topics are reviewed ranging from opinion and cultural and language dynamics to crowd behavior, hierarchy formation, human dynamics, and social spreading. The connections between these problems and other, more traditional, topics of statistical physics are highlighted. Comparison of model results with empirical data from social systems are also emphasized. |
2005 |
Castellano, Claudio; Loreto, Vittorio; Barrat, Alain; Cecconi, Federico; Parisi, Domenico Comparison of voter and Glauber ordering dynamics on networks (Journal Article) PHYSICAL REVIEW E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS, 71 , pp. 066107–066114, 2005. (Links | BibTeX | Tags: loreto, opinion_dynamics, social_dynamics, statistical_physics) @article{b, title = {Comparison of voter and Glauber ordering dynamics on networks}, author = {Claudio Castellano and Vittorio Loreto and Alain Barrat and Federico Cecconi and Domenico Parisi}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-27944464161&partnerID=65&md5=e33dbf7e41cab953b8225aab3fc4c019, http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=000230275000023&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=0c7ff228ccbaaa74236f48834a34396a}, year = {2005}, date = {2005-01-01}, journal = {PHYSICAL REVIEW E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS}, volume = {71}, pages = {066107--066114}, publisher = {AMERICAN PHYSICAL SOC, ONE PHYSICS ELLIPSE, COLLEGE PK, USA, MD, 20740-3844}, keywords = {loreto, opinion_dynamics, social_dynamics, statistical_physics}, pubstate = {published}, tppubtype = {article} } |
2004 |
Radicchi, Filippo; Castellano, Claudio; Cecconi, Federico; Loreto, Vittorio; Parisi, Domenico Defining and identifying communities in networks (Journal Article) PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 101 , pp. 2658–2663, 2004. (Abstract | Links | BibTeX | Tags: complex_networks, loreto, social_dynamics) @article{b, title = {Defining and identifying communities in networks}, author = {Filippo Radicchi and Claudio Castellano and Federico Cecconi and Vittorio Loreto and Domenico Parisi}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-1542357701&partnerID=65&md5=38349a61be5998bacd3283c61c6abce6, http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=000220065300004&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=0c7ff228ccbaaa74236f48834a34396a}, year = {2004}, date = {2004-01-01}, journal = {PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA}, volume = {101}, pages = {2658--2663}, publisher = {National Academy of Sciences:2101 Constitution Avenue Northwest:Washington, DC 20418:(877)314-2253, (615)377-3322, EMAIL: subspnas@nas.edu, INTERNET: http://www.pnas.org, Fax: (615)377-0525}, abstract = {The investigation of community structures in networks is an important issue in many domains and disciplines. This problem is relevant for social tasks (objective analysis of relationships on the web), biological inquiries (functional studies in metabolic and protein networks), or technological problems (optimization of large infrastructures). Several types of algorithms exist for revealing the community structure in networks, but a general and quantitative definition of community is not implemented in the algorithms, leading to an intrinsic difficulty in the interpretation of the results without any additional nontopological information. In this article we deal with this problem by showing how quantitative definitions of community are implemented in practice in the existing algorithms. In this way the algorithms for the identification of the community structure become fully self-contained. Furthermore, we propose a local algorithm to detect communities which outperforms the existing algorithms with respect to computational cost, keeping the same level of reliability. The algorithm is tested on artificial and real-world graphs. In particular, we show how the algorithm applies to a network of scientific collaborations, which, for its size, cannot be attacked with the usual methods. This type of local algorithm could open the way to applications to large-scale technological and biological systems.}, keywords = {complex_networks, loreto, social_dynamics}, pubstate = {published}, tppubtype = {article} } The investigation of community structures in networks is an important issue in many domains and disciplines. This problem is relevant for social tasks (objective analysis of relationships on the web), biological inquiries (functional studies in metabolic and protein networks), or technological problems (optimization of large infrastructures). Several types of algorithms exist for revealing the community structure in networks, but a general and quantitative definition of community is not implemented in the algorithms, leading to an intrinsic difficulty in the interpretation of the results without any additional nontopological information. In this article we deal with this problem by showing how quantitative definitions of community are implemented in practice in the existing algorithms. In this way the algorithms for the identification of the community structure become fully self-contained. Furthermore, we propose a local algorithm to detect communities which outperforms the existing algorithms with respect to computational cost, keeping the same level of reliability. The algorithm is tested on artificial and real-world graphs. In particular, we show how the algorithm applies to a network of scientific collaborations, which, for its size, cannot be attacked with the usual methods. This type of local algorithm could open the way to applications to large-scale technological and biological systems. |
Castellano, Claudio; Federico cecconi,; Loreto, Vittorio; Parisi, Domenico; Radicchi, Filippo Self-contained algorithms to detect communities in networks (Journal Article) THE EUROPEAN PHYSICAL JOURNAL. B, CONDENSED MATTER PHYSICS, 38 , pp. 311–319, 2004. (Links | BibTeX | Tags: complex_networks, loreto, social_dynamics) @article{b, title = {Self-contained algorithms to detect communities in networks}, author = {Claudio Castellano and Federico cecconi, and Vittorio Loreto and Domenico Parisi and Filippo Radicchi}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-2942534881&partnerID=65&md5=95c1d25779e19f22e6eacd483a47ec2b, http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=000221447300024&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=0c7ff228ccbaaa74236f48834a34396a}, year = {2004}, date = {2004-01-01}, journal = {THE EUROPEAN PHYSICAL JOURNAL. B, CONDENSED MATTER PHYSICS}, volume = {38}, pages = {311--319}, publisher = {EDP Sciences, Springer Verlag Germany}, keywords = {complex_networks, loreto, social_dynamics}, pubstate = {published}, tppubtype = {article} } |