Experimental Evaluation of Dynamic Shortest Path Tree Algorithms on Homogeneous Batches

In this paper we focus on dynamic batch algorithms for single-source shortest paths in graphs with positive real edge weights. A dynamic algorithm is called batch if it is able to handle graph changes that consist of multiple edge updates at a time, i.e., a batch. Unfortunately, most of the algorithmic solutions known in the literature for this problem are analyzed with respect to heterogeneous parameters, and this makes unfeasible an effective comparison on a theoretical basis. Thus, for a full comprehension of their actual performance, in the past these solutions have been assessed experimentally. In this paper, we move ahead along this direction, by focusing our attention on homogeneous batches, i.e., either incremental or decremental batches, which model realistic dynamic scenarios like node failures in communication networks and traffic jams in road networks. We provide an extensive experimental study including both the most effective previous batch algorithms and a recently developed one, which was explicitly designed (and was shown to be theoretically efficient) exactly for homogeneous batches. Our work complements previous studies and shows that the various solutions can be consistently ranked on the basis of the type of homogeneous batch and of the underlying network. As a result, we believe it can be helpful in selecting a proper solution depending on the specific application scenario.