Directed Percolation in Temporal Networks
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Abstract (arXiv)
Connectivity and reachability on temporal networks, which can describe the spreading of a disease, decimation of information or the accessibility of a public transport system over time, have been among the main contemporary areas of study in complex systems for the last decade. However, while isotropic percolation theory successfully describes connectivity in static networks, a similar description has not been yet developed for temporal networks. Here address this problem and formalize a mapping of the concept of temporal network reachability to percolation theory. We show that the limited-waiting-time reachability, a generic notion of constrained connectivity in temporal networks, displays directed percolation phase transition in connectivity. Consequently, the critical percolation properties of spreading processes on temporal networks can be estimated by a set of known exponents characterising the directed percolation universality class. This result is robust across a diverse set of temporal network models with different temporal and topological heterogeneities, while by using our methodology we uncover similar reachability phase transitions in real temporal networks too. These findings open up an avenue to apply theory, concepts and methodology from the well-developed directed percolation literature to temporal networks.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 Jul 2021.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 2; 2026: 1.
5 papers.
- 2022De Bruijn goes Neural: Causality-Aware Graph Neural Networks for Time Series Data on Dynamic Graphscited by 10
- 2025A Survey of Link Prediction in Temporal Networkscited by 6
- 2024Initialisation and Network Effects in Decentralised Federated Learningcited by 2
- 2025Weisfeiler and Leman Follow the Arrow of Time: Expressive Power of Message Passing in Temporal Event Graphscited by 2
- 2026Decentralised Federated Learning over Temporal Networks: The Role of Heterogeneitiescited by 0
- 2026Decentralised Federated Learning over Temporal Networks: The Role of Heterogeneities3 Jul 2026
- 2025Weisfeiler and Leman Follow the Arrow of Time: Expressive Power of Message Passing in Temporal Event Graphs30 May 2025
- 2025A Survey of Link Prediction in Temporal Networks28 Feb 2025
- 2024Initialisation and Network Effects in Decentralised Federated Learning23 Mar 2024
- 2022De Bruijn goes Neural: Causality-Aware Graph Neural Networks for Time Series Data on Dynamic Graphs17 Sep 2022