A parameter free similarity index based on clustering ability for link prediction in complex networks

TL;DR

This paper introduces a novel, parameter-free similarity index based on clustering ability for link prediction in complex networks, demonstrating high accuracy and efficiency over existing methods.

Contribution

It proposes a new similarity index based on clustering ability, which is efficient, parameter-free, and improves link prediction accuracy in complex networks.

Findings

Outperforms CN, AA, and RA similarity indices in accuracy.

Demonstrates high efficiency on real-world and modeled networks.

Effective in various network topologies.

Abstract

Link prediction in complex network based on solely topological information is a challenging problem. In this paper, we propose a novel similarity index, which is efficient and parameter free, based on clustering ability. Here clustering ability is defined as average clustering coefficient of nodes with the same degree. The motivation of our idea is that common-neighbors are able to contribute to the likelihood of forming a link because they own some ability of clustering their neighbors together, and then clustering ability defined here is a measure for this capacity. Experimental numerical simulations on both real-world networks and modeled networks demonstrated the high accuracy and high efficiency of the new similarity index compared with three well-known common-neighbor based similarity indices: CN, AA and RA.