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Dissecting Subgroups in Complex Networks

Published on June 4, 2018 by

Complex social, biological, or other networks often arise from a wide range of mechanisms, acting within a heterogeneous and often dynamic environment.  This complicated “stew” of factors gives rise to networks that are anything but clean and elegant: rather, they are decidedly “lumpy,” consisting of myriad overlapping subgroups of varying size and consistency.  While this heterogeneity can make analysis difficult, it can also provide clues to the drivers of network formation, since different mechanisms – and different features of the local environment – tend to produce groups of characteristic size and composition.  To unpack these generating mechanisms, one needs a way to “dissect” subgroups within larger networks and discover  how they relate to their members’ attributes.  In a new paper, NACSD lab alumnus Sean Fitzhugh and lab PI Carter Butts offer an approach to this problem.  The paper, which appears in the journal Social Networks, exploits an easily implemented, non-parametric technique to identify the ranges of subgroup sizes over which individuals with particular attributes are especially likely (or unlikely) to be found together.  Applying the method to Facebook friendship networks from a number of universities, Fitzhugh and Butts show that the method is able to detect idiosyncratic but important features of the social landscape – like the undergraduate housing systems used at schools like CalTech and Wellesley – that indirectly shape student friendship networks.  By looking for shared attributes that foster ties but that are not the building blocks of larger groups, Fitzhugh and Butts show how the precursors of bridging ties can be identified (in this case, attending the same high school ).  Armed with this new approach, network analysts can efficiently dissect networks ranging from friendship and advice to interactions among biomolecules, revealing clues to the hidden processes that give rise to them.

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