A belief propagation based hierarchical approach for capacitated network decomposition
Juan Liu, Huaiyu Dai · 2014
With increasing applications of large-scale networks, fully distributed and self-organized clustering algorithms have attracted much attention recently. In this paper, we review the capacitated network decomposition problem that aims to minimize the cut weight. As an extension of our previous work [1], we propose a novel belief propagation based distributed clustering algorithm, which allows each node to select one or multiple co-cluster nodes by sending messages between pairs of nodes. Based on the max-product algorithm, we derive the message-passing procedure on the corresponding factor graph. The distributed clustering algorithm is also extended for a hierarchical structure. Simulation results show that our algorithm can efficiently find a capacitated network decomposition solution, and it outperforms the popular affinity propagation algorithm in some applications.