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 |
Gravino, Pietro; Servedio, Vito; Barrat, Alain; Loreto, Vittorio Complex structures and semantics in free word association (Journal Article) ADVANCES IN COMPLEX SYSTEM, 15 , pp. 1250054–1250075, 2012. (Abstract | Links | BibTeX | Tags: complex_networks, gravino, language_dynamics, loreto, servedio) @article{b, title = {Complex structures and semantics in free word association}, author = {Pietro Gravino and Vito D.P. Servedio and Alain Barrat and Vittorio Loreto}, url = {http://www.worldscinet.com/acs/15/1503n04/S0219525912500543.html, http://www.scopus.com/inward/record.url?eid=2-s2.0-84861899272&partnerID=65&md5=4d6ecfe66508c0a3cf8bba8dae67c997 http://samarcanda.phys.uniroma1.it/vittorioloreto/PAPERS/2012/S0219525912500543.pdf}, year = {2012}, date = {2012-01-01}, journal = {ADVANCES IN COMPLEX SYSTEM}, volume = {15}, pages = {1250054--1250075}, publisher = {WORLD SCIENTIFIC PUBL CO PTE LTD}, abstract = {We investigate the directed and weighted complex network of free word associations in which players write a word in response to another word given as input. We analyze in details two large datasets resulting from two very different experiments: On the one hand the massive multiplayer web-based Word Association Game known as Human Brain Cloud, and on the other hand the South Florida Free Association Norms experiment. In both cases, the networks of associations exhibit quite robust properties like the small world property, a slight assortativity and a strong asymmetry between in-degree and out-degree distributions. A particularly interesting result concerns the existence of a characteristic scale for the word association process, arguably related to specific conceptual contexts for each word. After mapping, the Human Brain Cloud network onto the WordNet semantics network, we point out the basic cognitive mechanisms underlying word associations when they are represented as paths in an underlying semantic network. We derive in particular an expression describing the growth of the HBC graph and we highlight the existence of a characteristic scale for the word association process.}, keywords = {complex_networks, gravino, language_dynamics, loreto, servedio}, pubstate = {published}, tppubtype = {article} } We investigate the directed and weighted complex network of free word associations in which players write a word in response to another word given as input. We analyze in details two large datasets resulting from two very different experiments: On the one hand the massive multiplayer web-based Word Association Game known as Human Brain Cloud, and on the other hand the South Florida Free Association Norms experiment. In both cases, the networks of associations exhibit quite robust properties like the small world property, a slight assortativity and a strong asymmetry between in-degree and out-degree distributions. A particularly interesting result concerns the existence of a characteristic scale for the word association process, arguably related to specific conceptual contexts for each word. After mapping, the Human Brain Cloud network onto the WordNet semantics network, we point out the basic cognitive mechanisms underlying word associations when they are represented as paths in an underlying semantic network. We derive in particular an expression describing the growth of the HBC graph and we highlight the existence of a characteristic scale for the word association process. |
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} } |