Community Mining from Signed Social Networks Export

@article{citeulike:6074609, abstract = {Many complex systems in the real world can be modeled as signed social networks that contain both positive and negative relations. Algorithms for mining social networks have been developed in the past, however most of them were designed primarily for networks containing only positive relations and thus not suitable for signed networks. In this work, we propose a new algorithm, called FEC, to mine signed social networks so that both positive within-group relations and negative between-group relations are dense. FEC considers both the sign and the density of relations as the clustering attributes, making itself effective for not only signed networks but also conventional social networks including only positive relations. Also, FEC adopts an agent-based heuristic that makes the algorithm efficient (in linear time with respect to the size of a network) and capable of giving nearly optimal solutions. FEC depends on only one parameter whose value can easily be set, and requires no prior knowledge on hidden community structures. The effectiveness and efficacy of FEC have been demonstrated through a set of rigorous experiments involving both benchmark and randomly-generated signed networks.}, address = {Los Alamitos, CA, USA}, author = {Yang, Bo and Cheung, William and Liu, Jiming}, citeulike-article-id = {6074609}, citeulike-linkout-0 = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2007.1061}, citeulike-linkout-1 = {http://dx.doi.org/10.1109/TKDE.2007.1061}, doi = {10.1109/TKDE.2007.1061}, issn = {1041-4347}, journal = {IEEE Transactions on Knowledge and Data Engineering}, keywords = {community, learning, mining, network, social}, number = {10}, pages = {1333--1348}, posted-at = {2009-11-05 14:36:57}, priority = {2}, publisher = {IEEE Computer Society}, title = {Community Mining from Signed Social Networks}, url = {http://dx.doi.org/10.1109/TKDE.2007.1061}, volume = {19}, year = {2007} }

TY - JOUR ID - citeulike:6074609 L3 - citeulike-article-id:6074609 N2 - Many complex systems in the real world can be modeled as signed social networks that contain both positive and negative relations. Algorithms for mining social networks have been developed in the past, however most of them were designed primarily for networks containing only positive relations and thus not suitable for signed networks. In this work, we propose a new algorithm, called FEC, to mine signed social networks so that both positive within-group relations and negative between-group relations are dense. FEC considers both the sign and the density of relations as the clustering attributes, making itself effective for not only signed networks but also conventional social networks including only positive relations. Also, FEC adopts an agent-based heuristic that makes the algorithm efficient (in linear time with respect to the size of a network) and capable of giving nearly optimal solutions. FEC depends on only one parameter whose value can easily be set, and requires no prior knowledge on hidden community structures. The effectiveness and efficacy of FEC have been demonstrated through a set of rigorous experiments involving both benchmark and randomly-generated signed networks. IS - 10 JF - IEEE Transactions on Knowledge and Data Engineering SN - 1041-4347 AD - Los Alamitos, CA, USA EP - 1348 TI - Community Mining from Signed Social Networks PB - IEEE Computer Society VL - 19 SP - 1333 KW - community KW - learning KW - mining KW - network KW - social AU - Yang, Bo AU - Cheung, William AU - Liu, Jiming PY - 2007/// UR - http://dx.doi.org/10.1109/TKDE.2007.1061 ER -