Defining and identifying communities in networks Export

@article{citeulike:300529, abstract = {10.1073/pnas.0400054101 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.}, address = {Dipartimento di Fisica, Universit\`{a} di Roma Tor Vergata, Via della Ricerca Scientifica 1, 00133 Rome, Italy.}, author = {Radicchi, Filippo and Castellano, Claudio and Cecconi, Federico and Loreto, Vittorio and Parisi, Domenico}, citeulike-article-id = {300529}, citeulike-linkout-0 = {http://dx.doi.org/10.1073/pnas.0400054101}, citeulike-linkout-1 = {http://www.pnas.org/content/101/9/2658.abstract}, citeulike-linkout-2 = {http://www.pnas.org/content/101/9/2658.full.pdf}, citeulike-linkout-3 = {http://www.pnas.org/cgi/content/abstract/101/9/2658}, citeulike-linkout-4 = {http://view.ncbi.nlm.nih.gov/pubmed/14981240}, citeulike-linkout-5 = {http://www.hubmed.org/display.cgi?uids=14981240}, day = {2}, doi = {10.1073/pnas.0400054101}, issn = {0027-8424}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, keywords = {network-analysis}, month = {March}, number = {9}, pages = {2658--2663}, posted-at = {2007-02-27 13:27:25}, priority = {2}, title = {Defining and identifying communities in networks}, url = {http://dx.doi.org/10.1073/pnas.0400054101}, volume = {101}, year = {2004} }

TY - JOUR ID - citeulike:300529 L3 - citeulike-article-id:300529 N2 - 10.1073/pnas.0400054101 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. IS - 9 JF - Proceedings of the National Academy of Sciences of the United States of America SN - 0027-8424 AD - Dipartimento di Fisica, Università di Roma Tor Vergata, Via della Ricerca Scientifica 1, 00133 Rome, Italy. EP - 2663 TI - Defining and identifying communities in networks VL - 101 SP - 2658 KW - network-analysis AU - Radicchi, Filippo AU - Castellano, Claudio AU - Cecconi, Federico AU - Loreto, Vittorio AU - Parisi, Domenico PY - 2004/03/02/ UR - http://dx.doi.org/10.1073/pnas.0400054101 ER -