A novel similarity measure for mining missing links in long-path networks

TL;DR

This paper introduces a new similarity measure, SESPL, for link prediction in long-path networks, demonstrating superior effectiveness and efficiency over existing methods through extensive real-world network testing.

Contribution

The paper proposes SESPL, a novel similarity index tailored for long-path networks, and shows its improved performance and independence from traditional features using machine learning.

Findings

SESPL outperforms existing predictors in long-path networks.

SESPL achieves a 44.09% gain over GraphWave.

SESPL is an independent feature in similarity-based prediction space.

Abstract

Network information mining is the study of the network topology, which answers a large number of application-based questions towards the structural evolution and the function of a real system. For example, the questions can be related to how the real system evolves or how individuals interact with each other in social networks. Although the evolution of the real system may seem to be found regularly, capturing patterns on the whole process of the evolution is not trivial. Link prediction is one of the most important technologies in network information mining, which can help us understand the real system's evolution law. Link prediction aims to uncover missing links or quantify the likelihood of the emergence of nonexistent links from known network structures. Currently, widely existing methods of link prediction almost focus on short-path networks that usually have a myriad of close triangular structures. However, these algorithms on highly sparse or long-path networks have poor performance. Here, we propose a new index that is associated with the principles of Structural Equivalence and Shortest Path Length ($SESPL$) to estimate the likelihood of link existence in long-path networks. Through 548 real networks test, we find that $SESPL$ is more effective and efficient than other similarity-based predictors in long-path networks. We also exploit the performance of $SESPL$ predictor and embedding-based approaches via machine learning techniques, and the performance of $SESPL$ can achieve a gain of 44.09\% over $GraphWave$ and 7.93\% over $Node2vec$. Finally, according to the matrix of Maximal Information Coefficient ($MIC$) between all the similarity-based predictors, $SESPL$ is a new independent feature to the space of traditional similarity features.