A multi-attribute method for ranking influential nodes in complex networks

Abstract

Calculating the importance of influential nodes and ranking them based on their diffusion power is one of the open issues and critical research fields in complex networks. It is essential to identify an attribute that can compute and rank the diffusion power of nodes with high accuracy, despite the plurality of nodes and many relationships between them. Most methods presented only use one structural attribute to capture the influence of individuals, which is not entirely accurate in most networks. The reason is that network structures are disparate, and these methods will be inefficient by altering the network. A possible solution is to use more than one attribute to examine the characteristics aspect and address the issue mentioned. Therefore, this study presents a method for identifying and ranking node’s ability to spread information. The purpose of this study is to present a multi-attribute decision making approach for determining diffusion power and classification of nodes, which uses several local and semi-local attributes. Local and semi-local attributes with linear time complexity are used, considering different aspects of the network nodes. Evaluations performed on datasets of real networks demonstrate that the proposed method performs satisfactorily in allocating distinct ranks to nodes; moreover, as the infection rate of nodes increases, the accuracy of the proposed method increases.

Minimizing outbreak through targeted blocking for disease control: a community-based approach using super-spreader node identification

“…In the propagation of phenomena within complex network structures, two critical issues arise: (1) the optimal selection of a set of network nodes to minimize the spread rate (referred to as “super-blockers”) 2 , 10 ) the selection of a set of nodes to maximize the propagation rate (known as “Top-spreaders”) 11 , 12 . While some recent research treats these two issues as equivalent, closer examination reveals that super-blockers and Top-spreaders differ in nature.…”