Extracting the multiscale backbone of complex weighted networks Export

@article{citeulike:4293919, abstract = {10.1073/pnas.0808904106 A large number of complex systems find a natural abstraction in the form of weighted networks whose nodes represent the elements of the system and the weighted edges identify the presence of an interaction and its relative strength. In recent years, the study of an increasing number of large-scale networks has highlighted the statistical heterogeneity of their interaction pattern, with degree and weight distributions that vary over many orders of magnitude. These features, along with the large number of elements and links, make the extraction of the truly relevant connections forming the network's backbone a very challenging problem. More specifically, coarse-graining approaches and filtering techniques come into conflict with the multiscale nature of large-scale systems. Here, we define a filtering method that offers a practical procedure to extract the relevant connection backbone in complex multiscale networks, preserving the edges that represent statistically significant deviations with respect to a null model for the local assignment of weights to edges. An important aspect of the method is that it does not belittle small-scale interactions and operates at all scales defined by the weight distribution. We apply our method to real-world network instances and compare the obtained results with alternative backbone extraction techniques.}, author = {Serrano, M. \~{A}ngeles and Bogu\~{A}±\~{A}!`, Mari\~{A}!`n and Vespignani, Alessandro}, citeulike-article-id = {4293919}, citeulike-linkout-0 = {http://dx.doi.org/10.1073/pnas.0808904106}, citeulike-linkout-1 = {http://www.pnas.org/content/0808904106v1//.abstract}, citeulike-linkout-2 = {http://www.pnas.org/content/0808904106v1//.full.pdf}, citeulike-linkout-3 = {http://view.ncbi.nlm.nih.gov/pubmed/19357301}, citeulike-linkout-4 = {http://www.hubmed.org/display.cgi?uids=19357301}, day = {21}, doi = {10.1073/pnas.0808904106}, journal = {Proceedings of the National Academy of Sciences}, keywords = {allostery, graph\_theory, multiscale, systems\_biology}, month = {April}, number = {16}, pages = {6483--6488}, posted-at = {2009-04-23 12:30:54}, priority = {2}, title = {Extracting the multiscale backbone of complex weighted networks}, url = {http://dx.doi.org/10.1073/pnas.0808904106}, volume = {106}, year = {2009} }

TY - JOUR ID - citeulike:4293919 L3 - citeulike-article-id:4293919 N2 - 10.1073/pnas.0808904106 A large number of complex systems find a natural abstraction in the form of weighted networks whose nodes represent the elements of the system and the weighted edges identify the presence of an interaction and its relative strength. In recent years, the study of an increasing number of large-scale networks has highlighted the statistical heterogeneity of their interaction pattern, with degree and weight distributions that vary over many orders of magnitude. These features, along with the large number of elements and links, make the extraction of the truly relevant connections forming the network's backbone a very challenging problem. More specifically, coarse-graining approaches and filtering techniques come into conflict with the multiscale nature of large-scale systems. Here, we define a filtering method that offers a practical procedure to extract the relevant connection backbone in complex multiscale networks, preserving the edges that represent statistically significant deviations with respect to a null model for the local assignment of weights to edges. An important aspect of the method is that it does not belittle small-scale interactions and operates at all scales defined by the weight distribution. We apply our method to real-world network instances and compare the obtained results with alternative backbone extraction techniques. IS - 16 JF - Proceedings of the National Academy of Sciences EP - 6488 TI - Extracting the multiscale backbone of complex weighted networks VL - 106 SP - 6483 KW - allostery KW - graph_theory KW - multiscale KW - systems_biology AU - Serrano, Ángeles AU - Boguñá, Marián AU - Vespignani, Alessandro PY - 2009/04/21/ UR - http://dx.doi.org/10.1073/pnas.0808904106 ER -