Graph Energy Ranking for Scale-Free Networks using Barabasi-Albert Model

摘要

A social network is a vast collection of actors and interactions. It forms one of the complex networks. There are various types of social networks such as acquaintance networks, online social networks, covert networks, citation networks, and collaboration networks, etc. Most of these real-world networks are scale-free, and they follow a power-law distribution. Each of these networks has nodes which have various roles to play, and all nodes are not equally important. Hence we need to rank them based on their importance. In this paper, we propose an algorithm named Graph Energy Ranking (GER) to rank the nodes of scale-free networks built using the Barabasi-Albert model. GER analyses the impact of node deletion on the underlying network and therefore gives a better understanding of the network features. Study of ranking done by existing centrality measures versus GER is performed to observe the similarity in the ranking process.