Navigating temporal networks

@article{Lee2019NavigatingTN,
  title={Navigating temporal networks},
  author={Sang Hoon Lee and Petter Holme},
  journal={Physica A: Statistical Mechanics and its Applications},
  year={2019}
}
  • Sang Hoon Lee, P. Holme
  • Published 14 April 2018
  • Computer Science
  • Physica A: Statistical Mechanics and its Applications

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References

SHOWING 1-10 OF 34 REFERENCES
Exploring maps with greedy navigators
TLDR
This work presents a simple greedy spatial navigation strategy as a probe to explore spatial networks, and suggests that the centralities measures have to be modified to incorporate the navigators' behavior, and presents the intriguing effect of navigator' greediness where removing some edges may actually enhance the routing efficiency.
Navigability of temporal networks in hyperbolic space
TLDR
This work analyzes the navigability of real networks by using greedy routing in hyperbolic space, where the nodes are subject to a stochastic activation-inactivation dynamics, and finds that such dynamics enhances navigability with respect to the static case.
Exploring temporal networks with greedy walks
TLDR
Analysis of the structure of greedy walks indicates that burst trains, sequences of repeated contacts between node pairs, are the dominant factor, and results indicate a richness of correlated temporal-topological patterns in temporal networks.
Random walk centrality for temporal networks
TLDR
A centrality measure for temporal networks based on random walks under periodic boundary conditions that is called TempoRank, which is applied to human interaction networks and shows that although it is important for a node to be connected to another node with many random walkers at the right moment, this effect is negligible in practice when the time order of link activation is included.
Random walks on temporal networks.
TLDR
It is shown that the random walk exploration is slower on temporal networks than it is on the aggregate projected network, even when the time is properly rescaled, and a fundamental role is played by the temporal correlations between consecutive contacts present in the data.
Temporal Networks
Modern temporal network theory: a colloquium
TLDR
This colloquium reviews the methods to analyze and model temporal networks and processes taking place on them, focusing mainly on the last three years, which includes the spreading of infectious disease, opinions, rumors, in social networks; information packets in computer networks; various types of signaling in biology, and more.
Random walks and search in time-varying networks.
TLDR
This work considers a model for time-varying networks created from the activity potential of the nodes and derives solutions of the asymptotic behavior of random walks and the mean first passage time in undirected and directed networks.
Steady state and mean recurrence time for random walks on stochastic temporal networks
TLDR
This work theoretically study two types of event-driven random walks on a stochastic temporal network model that produces arbitrary distributions of interevent times and finds that the steady state is always the uniform density for the passive random walk.
Quantifying the effect of temporal resolution on time-varying networks
TLDR
This work quantifies the impact of an arbitrary Δt on the description of a dynamical process taking place upon a time-varying network, and focuses on the elementary random walk, and puts forth a simple mathematical framework that well describes the behavior observed on real datasets.
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