Complex networks

Friday, September 7, 2012 - 9:00am - 10:00am
Gyan Ranjan (University of Minnesota, Twin Cities)
We explore a geometric and topological approach to understanding the structural significance
of edges in a complex network. To do so, we embed the complex network (or the graph
$G(V, E)$ representing it) into a Euclidean space determined by the eigen-space of the
Moore-Penrose pseudo-inverse of the combinatorial laplacian (denoted by $\bb L^+(G)$).
The element $l^+_{ij}$ in $\bb L^+(G)$ ($i^{th} ~row-j^{th} ~column)$ determines the
angular distance between the position vectors of nodes $i$ and $j$ in this
Thursday, March 1, 2012 - 2:00pm - 2:45pm
Jennifer Neville (Purdue University)
Recently there has been a growing interest in representing data from complex systems as graphs and analyzing the network structure to understand key patterns/dependencies in the underlying system. This has fueled a large body of research on both models of network structure and algorithms to automatically discover patterns (e.g., communities) in the structure.
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