Improving local clustering based top-L link prediction methods via asymmetrical link clustering information

TL;DR

This paper introduces asymmetric link clustering (ALC) to enhance local clustering-based top-L link prediction methods, significantly improving accuracy and stability across various network types.

Contribution

It proposes the novel ALC coefficient to better capture link roles, improving existing clustering-based link prediction methods.

Findings

ALC-based methods outperform node clustering methods in diverse networks.

ALC improves prediction accuracy on food web, hamster friendship, and Internet networks.

ALC-based methods show stable performance in global and personalized tasks.

Abstract

Networks can represent a wide range of complex systems, such as social, biological and technological systems. Link prediction is one of the most important problems in network analysis, and has attracted much research interest recently. Many link prediction methods have been proposed to solve this problem with various technics. We can note that clustering information plays an important role in solving the link prediction problem. In previous literatures, we find node clustering coefficient appears frequently in many link prediction methods. However, node clustering coefficient is limited to describe the role of a common-neighbor in different local networks, because it can not distinguish different clustering abilities of a node to different node pairs. In this paper, we shift our focus from nodes to links, and propose the concept of asymmetric link clustering (ALC) coefficient. Further, we improve three node clustering based link prediction methods via the concept of ALC. The experimental results demonstrate that ALC-based methods outperform node clustering based methods, especially achieving remarkable improvements on food web, hamster friendship and Internet networks. Besides, comparing with other methods, the performance of ALC-based methods are very stable in both globalized and personalized top-L link prediction tasks.