Improving the accuracy of the k-shell method by removing redundant links-from a perspective of spreading dynamics

TL;DR

This paper improves the k-shell method's accuracy in identifying influential nodes by removing redundant links that form core-like groups, leading to better network core decomposition and spreading influence prediction.

Contribution

The study introduces a diffusion importance measure to filter redundant links, enhancing the k-shell method's accuracy in core detection and node influence ranking.

Findings

Filtering redundant links improves core detection accuracy.

Renewed coreness better predicts spreading influence.

Enhanced ranking algorithms based on the refined core structure.

Abstract

Recent study shows that the accuracy of the k-shell method in determining node coreness in a spreading process is largely impacted due to the existence of core-like group, which has a large k-shell index but a low spreading efficiency. Based on analysis of the structure of core-like groups in real-world networks, we discover that nodes in the core-like group are mutually densely connected with very few out-leaving links from the group. By defining a measure of diffusion importance for each edge based on the number of out-leaving links of its both ends, we are able to identify redundant links in the spreading process, which have a relatively low diffusion importance but lead to form the locally densely connected core-like group. After filtering out the redundant links and applying the k-shell method to the residual network, we obtain a renewed coreness for each node which is a more accurate index to indicate its location importance and spreading influence in the original network. Moreover, we find that the performance of the ranking algorithms based on the renewed coreness are also greatly enhanced. Our findings help to more accurately decompose the network core structure and identify influential nodes in spreading processes.