Predicting links in ego-networks using temporal information

TL;DR

This paper introduces a temporal feature-based machine learning approach for link prediction in ego-networks, demonstrating improved accuracy by leveraging interaction timing data rather than structural information.

Contribution

It proposes novel temporal features for link prediction in ego-networks and shows their effectiveness on real-world cellphone interaction data.

Findings

Temporal features outperform structural features in prediction accuracy.

Temporal profile of interactions is a key predictor.

Elapsed time between contacts significantly improves predictions.

Abstract

Link prediction appears as a central problem of network science, as it calls for unfolding the mechanisms that govern the micro-dynamics of the network. In this work, we are interested in ego-networks, that is the mere information of interactions of a node to its neighbors, in the context of social relationships. As the structural information is very poor, we rely on another source of information to predict links among egos' neighbors: the timing of interactions. We define several features to capture different kinds of temporal information and apply machine learning methods to combine these various features and improve the quality of the prediction. We demonstrate the efficiency of this temporal approach on a cellphone interaction dataset, pointing out features which prove themselves to perform well in this context, in particular the temporal profile of interactions and elapsed time between contacts.