Pruning social networks using structural properties and descriptive attributes

Scale is often an issue with understanding and making sense of large social networks. Here we investigate methods for pruning social networks by determining the most relevant relationships. We measure importance in terms of predictive accuracy on a set of target attributes of the social network. Our goal is to create a pruned network that models only the most informative affiliations and relationships. We present methods for pruning networks based on both structural properties and descriptive attributes demonstrate it on a network of NASDAQ and NYSE businesses and on a bibliographic network.