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*Subject*: Network Science Institute Seminar - Marián Boguñá (University of Barcelona) - Nov 10 at 11 a.m.*From*: "Yenidede Kozcaz, Ilknur" <i yenidedekozcaz neu edu>*Date*: Fri, 6 Nov 2015 19:31:03 +0000*Accept-language*: en-US*Authentication-results*: spf=none (sender IP is ) smtp.mailfrom=i yenidedekozcaz neu edu;*List-archive*: <http://cosmos.phy.tufts.edu/mhonarc/bapc/>*List-help*: <mailto:bapc-request@cosmos.phy.tufts.edu?subject=help>*List-id*: Boston Area Physics Calendar <bapc.cosmos.phy.tufts.edu>*List-subscribe*: <https://cosmos.phy.tufts.edu/mailman/listinfo/bapc>, <mailto:bapc-request@cosmos.phy.tufts.edu?subject=subscribe>*List-unsubscribe*: <https://cosmos.phy.tufts.edu/mailman/options/bapc>, <mailto:bapc-request@cosmos.phy.tufts.edu?subject=unsubscribe>*Spamdiagnosticmetadata*: NSPM*Spamdiagnosticoutput*: 1:23*Thread-index*: AQHRGMmsoLvOcru3KkCxJzikLrxVbQ==*Thread-topic*: Network Science Institute Seminar - Marián Boguñá (University of Barcelona) - Nov 10 at 11 a.m.

by
University of Barcelona, Spain
Self-similarity is defined in a wide sense as the property of some systems to be, either exactly or statistically, similar to a part of themselves. This property is found in certain geometric objects that are intrinsically
embedded in metric spaces, so that distance in the metric space gives a natural standard of measurement to uncover similar patterns at different observation scales. In complex networks, the definition of self-similarity is not obvious since many networks
are not explicitly embedded in any physical geometry. In the absence of a natural geometry, the main problem in the definition of self-similarity stems from the fact that there is, a priori, no way to decide what is the part of the system that should be compared
to (and look alike) the whole. In this sense, self-similarity is not an intrinsic property of the system but it is directly related to the specific procedure to identify the appropriate subsystem. In this talk, I will explain how to define self-similarity
in ensembles of networks and multiplexes. Self-similarity has important implications in the global structure of networks and, in particular, in their vulnerability to failures of their constituents. For instance, self-similarity alone ‹independently of the
divergence of the second moment of the degree distribution‹ explains the absence of a percolation threshold in random scale-free networks. In the case of self-similar multiplexes, we show that interlayer degree correlations can change completely their global
connectivity properties, so that they can even recover a zero percolation threshold and a continuous transition in the thermodynamic limit, qualitatively exhibiting thus the ordinary percolation properties of noninteracting networks.
Host: Dima Krioukov, Associate Professor
ADDRESSCenter for Complex Network Research -
177 Huntington Avenue, Boston, MA 02115
11
^{th} floor http://www.barabasilab.com/ |

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