Fast Multiplex Graph Association Rules for Link Prediction

TL;DR

This paper introduces a fast, efficient method using multiplex graph association rules for link prediction in complex networks, enhancing prediction capabilities and providing insights into social balance in signed networks.

Contribution

It presents a novel, scalable framework for multiplex link prediction using graph association rules, enabling higher-order structure analysis and improved prediction accuracy.

Findings

Outperforms previous methods in runtime efficiency.

Predicts new node arrivals and higher-order links.

Provides insights into social balance in signed networks.

Abstract

Multiplex networks allow us to study a variety of complex systems where nodes connect to each other in multiple ways, for example friend, family, and co-worker relations in social networks. Link prediction is the branch of network analysis allowing us to forecast the future status of a network: which new connections are the most likely to appear in the future? In multiplex link prediction we also ask: of which type? Because this last question is unanswerable with classical link prediction, here we investigate the use of graph association rules to inform multiplex link prediction. We derive such rules by identifying all frequent patterns in a network via multiplex graph mining, and then score each unobserved link's likelihood by finding the occurrences of each rule in the original network. Association rules add new abilities to multiplex link prediction: to predict new node arrivals, to consider higher order structures with four or more nodes, and to be memory efficient. We improve over previous work by creating a framework that is also efficient in terms of runtime, which enables an increase in prediction performance. This increase in efficiency allows us to improve a case study on a signed multiplex network, showing how graph association rules can provide valuable insights to extend social balance theory.