WEM: A Node Importance Algorithm in Weighted Networks

TL;DR

This paper introduces WEM, a new node importance algorithm for weighted networks that improves accuracy and efficiency by incorporating weight contributions and dynamic programming, suitable for large-scale networks.

Contribution

The paper presents WEM, a novel weighted node importance algorithm that leverages uncertain graph techniques and dynamic programming for enhanced accuracy and reduced computational complexity.

Findings

WEM achieves higher accuracy in node importance ranking.

WEM has lower computational complexity suitable for large networks.

Experimental results validate the effectiveness of WEM.

Abstract

In view of the node importance in weighted networks, weighted expected method (WEM), was proposed in this paper, which take an advantages of uncertain graph algorithm. First, a weight processing method is proposed based on the relationship between the weight of edges and the intensity of contact between nodes, and the calculation method of the contribution of the weight of edges to the node importance is defined, even if there are two converse situations in the reality. Then, because of the use of dynamic programming method, which reduces the complexity of computational time to a linear level, WEM will be more suitable for the calculation in large weighted networks. Owning to its features of calculating, WEM can, to the greatest extent, ensure a precise order of node essential scores for each node. The node connectivity experiment and SIR simulation experiment show that the WEM has higher accuracy and relatively low time complexity.