Uncovering multi-order Popularity and Similarity Mechanisms in Link Prediction by graphlet predictors

TL;DR

This paper introduces a novel link prediction method using graphlet orbit degrees to represent popularity and similarity mechanisms, enhancing accuracy and interpretability across diverse real-world networks.

Contribution

It develops a multi-order orbit-degree-based predictor and a supervised learning model that fuses these features, providing new insights into network formation mechanisms.

Findings

Homophily dominates social networks with 91% win rate.

Different similarity features are prominent in economic, technological, and information networks.

No single feature dominates biological and transportation networks.

Abstract

Link prediction has become a critical problem in network science and has thus attracted increasing research interest. Popularity and similarity are two primary mechanisms in the formation of real networks. However, the roles of popularity and similarity mechanisms in link prediction across various domain networks remain poorly understood. Accordingly, this study used orbit degrees of graphlets to construct multi-order popularity- and similarity-based network link predictors, demonstrating that traditional popularity- and similarity-based indices can be efficiently represented in terms of orbit degrees. Moreover, we designed a supervised learning model that fuses multiple orbit-degree-based features and validated its link prediction performance. We also evaluated the mean absolute Shapley additive explanations of each feature within this model across 550 real-world networks from six domains. We observed that the homophily mechanism, which is a similarity-based feature, dominated social networks, with its win rate being 91\%. Moreover, a different similarity-based feature was prominent in economic, technological, and information networks. Finally, no single feature dominated the biological and transportation networks. The proposed approach improves the accuracy and interpretability of link prediction, thus facilitating the analysis of complex networks.