Detecting the overlapping and hierarchical community structure of complex networks Export

@article{citeulike:2570757, abstract = {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 communities. Here we present the first algorithm that finds both overlapping communities and the hierarchical structure. The method is based on the local optimization of a fitness function. Community structure is revealed by peaks in the fitness histogram. The resolution can be tuned by a parameter enabling to investigate different hierarchical levels of organization. Tests on real and artificial networks give excellent results.}, archivePrefix = {arXiv}, author = {Lancichinetti, Andrea and Fortunato, Santo and Kertesz, Janos}, citeulike-article-id = {2570757}, citeulike-linkout-0 = {http://arxiv.org/abs/0802.1218}, citeulike-linkout-1 = {http://arxiv.org/pdf/0802.1218}, citeulike-linkout-2 = {http://dx.doi.org/10.1088/1367-2630/11/3/033015}, day = {11}, doi = {10.1088/1367-2630/11/3/033015}, eprint = {0802.1218}, keywords = {clustering, interactome, overlap}, month = {Mar}, posted-at = {2009-04-15 20:07:28}, priority = {5}, title = {Detecting the overlapping and hierarchical community structure of complex networks}, url = {http://dx.doi.org/10.1088/1367-2630/11/3/033015}, year = {2009} }

TY - JOUR ID - citeulike:2570757 L3 - citeulike-article-id:2570757 N2 - 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 communities. Here we present the first algorithm that finds both overlapping communities and the hierarchical structure. The method is based on the local optimization of a fitness function. Community structure is revealed by peaks in the fitness histogram. The resolution can be tuned by a parameter enabling to investigate different hierarchical levels of organization. Tests on real and artificial networks give excellent results. TI - Detecting the overlapping and hierarchical community structure of complex networks KW - clustering KW - interactome KW - overlap AU - Lancichinetti, Andrea AU - Fortunato, Santo AU - Kertesz, Janos PY - 2009/Mar/11/ UR - http://dx.doi.org/10.1088/1367-2630/11/3/033015 ER -