A Guide to Temporal Networks

By Naoki Masuda, Renaud Lambiotte. Series on Complexity Science, 2016

313 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/b3e7622ed7a17e064346ca267cb9a1de4f100015

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. M. A. Porter
2. Naoki Masuda
3. Giulio Rossetti
4. Joakim Skarding
5. Bogdan Gabrys
6. Renaud Lambiotte
7. Katarzyna Musial
8. Rémy Cazabet

In Signal Processing

1. Bogdan Gabrys
2. Renaud Lambiotte
3. Katarzyna Musial
4. M. A. Porter
5. Rémy Cazabet
6. Giulio Rossetti

Citations in the corpus

• Random walks and diffusion on networks (2016, background): “In this section, we discuss some situations in which a temporal network is given in the form of a sequence of static networks (which are called “snapshots” in [59])(5).”
• Foundations and Modeling of Dynamic Networks Using Dynamic Graph Neural Networks: A Survey (2020, background): “These networks are studied in the temporal network literature and often represented as contact sequences [2], [9].”
• Community Discovery in Dynamic Networks (2017, background): “Finally, in (Masuda and Lambiotte, 2016), the authors identify two different typologies of community discovery approaches in temporal networks: • approaches focused on determining Community evolution that, with certain statistical accuracy, aim to detect when and how a reorganisations of communities…”