Observe Locally Rank Globally

Abstract

Most real world dynamic networks are evolving very fast with time. It is not feasible to collect the entire network at any given time to study its characteristics. This creates the need to propose local algorithms to study various properties of the network. In the present work, we estimate degree rank of a node without having the entire network. The proposed methods are based on the power law degree distribution characteristic or sampling techniques. We further study the efficiency and feasibility of these approaches in different contexts. The proposed methods are simulated on synthetic networks, as well as on real world social networks. Results show that the degree rank of a node can be estimated with high accuracy using only 1% samples of the network size. The accuracy of the estimation decreases from high ranked to low ranked nodes.