Community Detection and Link Prediction via Cluster-driven
Low-rank Matrix Completion
Abstract
Community detection and link prediction are
highly dependent since knowing cluster structure
as a priori will help identify missing links, and in
return, clustering on networks with supplemented
missing links will improve community detection
performance. In this paper, we propose a Clusterdriven Low-rank Matrix Completion (CLMC), for
performing community detection and link prediction simultaneously in a unified framework. To this
end, CLMC decomposes the adjacent matrix of a
target network as three additive matrices: clustering matrix, noise matrix and supplement matrix.
The community-structure and low-rank constraints
are imposed on the clustering matrix, such that the
noisy edges between communities are removed and
the resulting matrix is an ideal block-diagonal matrix. Missing edges are further learned via low-rank
matrix completion. Extensive experiments show
that CLMC achieves state-of-the-art performance.