Link prediction in dynamic networks using random dot product graphs

TL;DR

This paper presents a statistical approach using random dot product graphs for link prediction in dynamic networks, combining spectral methods and time series models to improve accuracy and understand network evolution.

Contribution

It extends static random dot product graph models to dynamic settings, integrating spectral and temporal modeling for improved link prediction.

Findings

Effective in simulated networks

Promising results on real-world data

Enhanced understanding of network evolution

Abstract

The problem of predicting links in large networks is an important task in a variety of practical applications, including social sciences, biology and computer security. In this paper, statistical techniques for link prediction based on the popular random dot product graph model are carefully presented, analysed and extended to dynamic settings. Motivated by a practical application in cyber-security, this paper demonstrates that random dot product graphs not only represent a powerful tool for inferring differences between multiple networks, but are also efficient for prediction purposes and for understanding the temporal evolution of the network. The probabilities of links are obtained by fusing information at two stages: spectral methods provide estimates of latent positions for each node, and time series models are used to capture temporal dynamics. In this way, traditional link prediction methods, usually based on decompositions of the entire network adjacency matrix, are extended using temporal information. The methods presented in this article are applied to a number of simulated and real-world graphs, showing promising results.