Fast Dominating Set Algorithms for Social Networks

In this paper we introduce two novel algorithms that are able to efficiently determine an approximation to the minimum dominating set problem, and at the same time, they will preserve the quality of the solution to an acceptable level. We compare these two algorithms with three existing algorithms, for a large number of synthetic datasets, and for several real world social networks. For experiments, we use social network generators that create both power-law and random networks, and a few real network datasets made available by the Stanford Network Analysis Project. Our experiments show that the proposed algorithms are viable, and in many instances, preferable for determining the minimum dominating set of a social network.