Parameterized Centrality for Network Analysis Export

@article{citeulike:6088467, abstract = {Bonacich centrality measures the number of attenuated paths between nodes in a network. We use this metric to study network structure, specifically, to rank nodes and find community structure of the network. To this end we extend the modularity-maximization method for community detection to use this centrality metric as a measure of node connectivity. Bonacich centrality contains a tunable parameter that sets the length scale of interactions. By studying how rankings and discovered communities change when this parameter is varied allows us to identify globally important nodes and structures. We apply the proposed method to several benchmark networks and show that it leads to better insight into network structure than earlier methods.}, archivePrefix = {arXiv}, author = {Lerman, Kristina and Ghosh, Rumi}, citeulike-article-id = {6088467}, citeulike-linkout-0 = {http://arxiv.org/abs/0911.1095}, citeulike-linkout-1 = {http://arxiv.org/pdf/0911.1095}, day = {5}, eprint = {0911.1095}, keywords = {centrality, graph, network}, month = {Nov}, posted-at = {2009-11-09 14:15:05}, priority = {2}, title = {Parameterized Centrality for Network Analysis}, url = {http://arxiv.org/abs/0911.1095}, year = {2009} }

TY - JOUR ID - citeulike:6088467 L3 - citeulike-article-id:6088467 N2 - Bonacich centrality measures the number of attenuated paths between nodes in a network. We use this metric to study network structure, specifically, to rank nodes and find community structure of the network. To this end we extend the modularity-maximization method for community detection to use this centrality metric as a measure of node connectivity. Bonacich centrality contains a tunable parameter that sets the length scale of interactions. By studying how rankings and discovered communities change when this parameter is varied allows us to identify globally important nodes and structures. We apply the proposed method to several benchmark networks and show that it leads to better insight into network structure than earlier methods. TI - Parameterized Centrality for Network Analysis KW - centrality KW - graph KW - network AU - Lerman, Kristina AU - Ghosh, Rumi PY - 2009/Nov/05/ UR - http://arxiv.org/abs/0911.1095 ER -