  Community and cluster detection is a popular field of social network
analysis. Most algorithms focus on static graphs or series of snapshots.
  In this paper we present an algorithm, which detects communities in dynamic
graphs. The method is based on shortest paths to high-connected nodes, so
called hubs. Due to local message passing we can update the clustering results
with low computational power.
  The presented algorithm is compared with other for some static social
networks. The reached modularity is not as high as the Louvain method, but even
higher then spectral clustering. For large-scale real-world datasets with given
ground truth, we could reconstruct most of the given community structure. The
advantage of the algorithm is the good performance in dynamic scenarios.
