[UCI-Calit2] Large-Scale Structure in Complex Networks - March 12

Anna Lynn Spitzer aspitzer at calit2.uci.edu
Wed Mar 10 15:13:59 PST 2010


Large-Scale Structure in Complex Networks - Methods of Detection and their Theoretical Limitations

 

Time:             1:30-3:20 p.m.

Date:             Friday, March 12, 2009

 

Speaker:          Jörg Reichardt, Institute of Theoretical Physics, University of Wuerzburg; Postdoc at UC Davis, Physics dept.

                  URL for this wiki: http://bit.ly/axwM12

                  

Location:         Reichardt will speak from 260 Galbraith Hall, UCSD; talk will be simulcast at:

   

 

                  3030 Anteater I&R Bldg (AIRB) UCI 

                  285 Powell Library, UCLA            

                  250 Olson Hall, UC DAVIS 

 

Abstract: Not all nodes are created equal in complex networks. Rather, they play diverse roles in the functioning of a network and their role is reflected in the network's link structure. Hence, structural analysis can be used to infer the latent roles and functions of nodes purely based on connectivity data. Currently, network structure is studied at three different levels. At the macro level, global network properties such as degree distributions, path-lengths, diameters or clustering coefficients are investigated. At the micro level, properties of individual nodes and edges such as centrality indices or rank functions such as page-rank are studied. The study of the meso-scale, which aims at studying joint properties of groups of nodes, so far has mainly been focused on the detection of cohesive subgroups of nodes, so-called communities.

 

The talk will show that, though important, communities are only one special case of a much wider class of mesoscopic structures called "stochastic block structures." This name comes from the fact that latent classes of roles and their resultant patterns of connectivity in a network account for salient block structure in the adjacency matrix of a network when the rows and columns are ordered according to these latent roles.

 

Reichardt presents an effective and accurate algorithm that performs this task employing a purely Bayesian approach, shows that it outperforms competing approaches and presents applications to real-world data sets that open new frontiers of research in the study of both structure, function and evolution of complex networks from a mesoscopic perspective.

 

Additionally, he investigates the limits of such purely data driven approaches to the detection of latent structure in networks using the toolbox of statistical mechanics. He will show that there exists a phase transition between detectable and in-principle undetectable structure in sparse networks and present approximation formulas to calculate the transition point and the shape of the transition.

 

More information: Doug White, drwhite at uci.edu

 

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Streaming videos for all past talks (2005-2009) are at http://intersci.ss.uci.edu/wiki/index.php/Current_events#HSC_Past_talks

 

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