Adjusting assortativity in complex networks

Abstract

Assortativity has been widely studied for understanding the structure and function of complex networks. Assortative is defined as an association of items with other items having similar characteristics. The research has shown that assortativity has a significant impact on many processes on networks, including information cascades, spreading, congestion relief, longevity, and epidemic thresholds. The degree distribution is also an important factor that affects some of these results. In this paper, we introduce a simple but effective method for adjusting a given network while preserving the degree distribution of the network and, if desired, the connectivity of the network. The algorithm is tested on both theoretical and real-world networks and is supported by detailed empirical results. We illustrate how changing assortativity affects some network properties. The method can be useful for researchers interested in the relationship of assortativity to network structures and the dynamics of processes on networks.