A Game-Theoretic Algorithm for Link Prediction

TL;DR

This paper introduces a novel game-theoretic, quasi-local algorithm for link prediction in networks, which effectively balances computational efficiency and accuracy, outperforming existing methods on real-world data.

Contribution

It presents a new quasi-local approach combining centrality and interaction indices, with fast algorithms and robust performance even with suboptimal parameters.

Findings

Outperforms state-of-the-art link prediction methods on real networks

Effective even with suboptimal radius parameter k

Provides fast algorithms for computing the proposed measure

Abstract

Predicting edges in networks is a key problem in social network analysis and involves reasoning about the relationships between nodes based on the structural properties of a network. In particular, link prediction can be used to analyse how a network will develop or - given incomplete information about relationships - to discover "missing" links. Our approach to this problem is rooted in cooperative game theory, where we propose a new, quasi-local approach (i.e., one which considers nodes within some radius k) that combines generalised group closeness centrality and semivalue interaction indices. We develop fast algorithms for computing our measure and evaluate it on a number of real-world networks, where it outperforms a selection of other state-of-the-art methods from the literature. Importantly, choosing the optimal radius k for quasi-local methods is difficult, and there is no assurance that the choice is optimal. Additionally, when compared to other quasi-local methods, ours achieves very good results even when given a suboptimal radius k as a parameter.