  An important challenge in big data analysis nowadays is detection of cohesive
groups in large-scale networks, including social networks, genetic networks,
communication networks and so. In this paper, we propose LabelRank, an
efficient algorithm detecting communities through label propagation. A set of
operators is introduced to control and stabilize the propagation dynamics.
These operations resolve the randomness issue in traditional label propagation
algorithms (LPA), stabilizing the discovered communities in all runs of the
same network. Tests on real-world networks demonstrate that LabelRank
significantly improves the quality of detected communities compared to LPA, as
well as other popular algorithms.
