A Hierarchical Method for Estimating Relative Importance in Complex Networks

Many classical algorithms for node importance estimating have already been developed over the past decades. However, these algorithms face difficulties in complex networks because of their large-scale nodes and complex relationship. We introduce a concept of i-level importance based on which we present a hierarchical method for estimating relative importance in complex networks. Most of complex networks are constructed with hierarchy inherently, and we could commit a hierarchical partition on them. We equate the node importance with the cluster importance in its parent component, which could scale-down computation, and be easier to be accepted.