Efficient approximation algorithms to determine minimum partial dominating sets in social networks

In this paper we report on extensive experiments for determining partial dominating sets of small size for various types of real and synthetic social networks. Our experiments ran on several real network datasets made available by the Stanford Network Analysis Project and on some synthetic power-law and random networks created with social network generators. To compute partial dominating sets on these networks we used five algorithms compared in [4], which were adapted for partial dominating sets. Our experiments showed that there are several good algorithms that can efficiently find quality approximations for the minimum-size partial dominating set problem. The best algorithm choice is dependent on the network characteristics and the value of the coverage parameter.