  With a core-periphery structure of networks, core nodes are densely
interconnected, peripheral nodes are connected to core nodes to different
extents, and peripheral nodes are sparsely interconnected. Core-periphery
structure composed of a single core and periphery has been identified for
various networks. However, analogous to the observation that many empirical
networks are composed of densely interconnected groups of nodes, i.e.,
communities, a network may be better regarded as a collection of multiple cores
and peripheries. We propose a scalable algorithm to detect multiple
non-overlapping groups of core-periphery structure in a network. We illustrate
our algorithm using synthesised and empirical networks. For example, we find
distinct core-periphery pairs with different political leanings in a network of
political blogs and separation between international and domestic subnetworks
of airports in some single countries in a world-wide airport network.
