Modularity is designed to measure the strength of division of a network into clusters (known also as communities). Networks with high modularity have dense connections between the vertices within clusters but sparse connections between vertices of different clusters. As a result, modularity is often used in optimization methods for detecting community structure in networks, and so it is an important graph parameter from a practical point of view. Unfortunately, many existing non-spatial models of complex networks do not generate graphs with high modularity; on the other hand, spatial models naturally create clusters. We investigate this phenomenon by considering a few examples from both sub-classes.

Speaker bio:

Dr. Pawel Pralat, PhD (http://www.math.ryerson.ca/~pralat/) is an Associate Professor at Ryerson University and the Assistant Director of Industry Liaison at The Fields Institute for Research in Mathematical Sciences. His main research interests are in modelling and mining complex networks. Since 2006, he has written 140+ papers with 100+ collaborators. He is trained both in (theoretical and applied) computer science as well as mathematics (M.Eng. and M.A.Sc. in CS, Ph.D. in Mathematics and CS), has strong programming and applied research skills, gained through experience in collaboration with the private sector (Microsoft Research, Google Research, Motorola, The Globe and Mail, BlackBerry, Alcatel-Lucent, Environics Analytics, etc.) as well as the Government of Canada (Tutte Institute for Mathematics and Computing, part of Communications Security Establishment).