M. Puck Rombach, Mason A. Porter, James H. Fowler, Peter J. Mucha
Intermediate-scale (or 'meso-scale') structures in networks have received
considerable attention, as the algorithmic detection of such structures makes
it possible to discover network features that are not apparent either at the
local scale of nodes and edges or at the global scale of summary statistics.
Numerous types of meso-scale structures can occur in networks, but
investigations of meso-scale network features have focused predominantly on the
identification and study of community structure. In this paper, we develop a
new method to investigate the meso-scale feature known as coreperiphery
structure, which consists of an identification of a network's nodes into a
densely connected core and a sparsely connected periphery. In contrast to
traditional network communities, the nodes in a core are also reasonably
well-connected to those in the periphery. Our new method of computing
core-periphery structure can identify multiple cores in a network and takes
different possible cores into account, thereby enabling a detailed description
of core-periphery structure. We illustrate the differences between our method
and existing methods for identifying which nodes belong to a core, and we use
it to classify the most important nodes using examples of friendship,
collaboration, transportation, and voting networks.
View original:
http://arxiv.org/abs/1202.2684
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