Hamidreza Mahyar
Hamid R. Rabiee
Ali Movaghar
ABSTRACT
One of the most relevant characteristics of social networks is community structure, in which network nodes are joined together in densely connected groups between which there are only sparser links. Uncovering these sparse links (i.e. intercommunity links) has a significant role in community detection problem which has been of great importance in sociology, biology, and computer science. In this paper, we propose a novel approach, called CS-ComDet, to efficiently detect the inter-community links based on a newly emerged paradigm in sparse signal recovery, called compressive sensing. We test our method on real-world networks of various kinds whose community structures are already known, and illustrate that the proposed method detects the inter-community links accurately even with low number of measurements (i.e. when the number of measurements is less than half of the number of existing links in the network).
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