Navigating temporal networks

TL;DR

This paper investigates navigation strategies on temporal networks, demonstrating that exploiting temporal information improves navigation efficiency and that node degree and burstiness significantly influence navigability.

Contribution

It introduces and compares greedy and non-exploitative navigation strategies on empirical temporal networks, highlighting the importance of temporal and topological correlations.

Findings

Greedy navigation outperforms reference strategies on real data.

Node degree and burstiness strongly correlate with navigability.

Temporal correlations can be exploited for more efficient navigation.

Abstract

Navigation on graphs is the problem how an agent walking on the graph can get from a source to a target with limited information about the graph. The information and the way to exploit it can vary. In this paper, we study navigation on temporal networks -- networks where we have explicit information about the time of the interaction, not only who interacts with whom. We contrast a type of greedy navigation -- where agents follow paths that would have worked well in the past -- with two strategies that do not exploit the additional information. We test these on empirical temporal network data sets. The greedy navigation is indeed more efficient than the reference strategies, meaning that there are correlations in the real temporal networks that can be exploited. We find that the navigability for individual nodes is most strongly correlated with degree and burstiness, i.e., both topological and temporal structures affect the navigation efficiency.