AN ADAPTIVE RANDOM WALK BASED DISTRIBUTED CLUSTERING ALGORITHM
In this paper, we present a fully distributed random walk based clustering algorithm intended to work on dynamic networks of arbitrary topologies. A bounded-size core is built through a random walks based procedure. Its neighboring nodes that do not belong to any cluster are recruited by the core as ordinary nodes. Using cores allow us to formulate constraints on the clustering and fulfill them on any topology. The correctness and termination of our algorithm are proven. We also prove that when two clusters are adjacent, atleast one of them has a complete core (i.e. a core with the maximum size allowed by the parameter). One of the important advantages of our mobility-adaptive algorithm is that the re-clustering is local: the management of the connections or disconnections of links and reorganization of nodes affect only the clusters in which they are, possibly adjacent clusters, and at worst, the ordinary nodes of the clusters adjacent to the neighboring clusters. This allows us to bound the diameter of the portion of the network that is affected by a topological change.
adaptive random walks clustering dynamic networks distributed algorithms mobility