Increasing paths in random temporal graphs

We consider random temporal graphs, a version of the classical Erdős–Rényi random graph G(n,p) where additionally, each edge has a distinct random time stamp, and connectivity is constrained to sequences of edges with increasing time stamps. We study the asymptotics for the distances in such graphs, mostly in the regime of interest where np is of order logn. We establish the first order asymptotics for the lengths of increasing paths: the lengths of the shortest and longest paths between typical vertices, the maxima of these lengths from a given vertex, as well as the maxima between any two vertices; this covers the (temporal) diameter.

Authors

Institutions

Publication Details

Journal
The Annals of Applied Probability
Published
2024-12-01
DOI
https://doi.org/10.1214/24-aap2097
Citations
2
Primary Topic
Opportunistic and Delay-Tolerant Networks
Type
article
Field-Weighted Citation Impact
0.70
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Increasing paths in random temporal graphs

Gábor Lugosi, Nina Kamčev, Nicolas Broutin
2 citations
The Annals of Applied Probability
Opportunistic and Delay-Tolerant Networks
0.70
article

Increasing paths in random temporal graphs

Gábor Lugosi, Nina Kamčev, Nicolas Broutin
article en
2 citations

Abstract

We consider random temporal graphs, a version of the classical Erdős–Rényi random graph G(n,p) where additionally, each edge has a distinct random time stamp, and connectivity is constrained to sequences of edges with increasing time stamps. We study the asymptotics for the distances in such graphs, mostly in the regime of interest where np is of order logn. We establish the first order asymptotics for the lengths of increasing paths: the lengths of the shortest and longest paths between typical vertices, the maxima of these lengths from a given vertex, as well as the maxima between any two vertices; this covers the (temporal) diameter.

The Annals of Applied ProbabilityVol. 34(6)
Institució Catalana de Recerca i Estudis Avançats (ES), University of Zagreb (HR), Sorbonne Université (FR), Laboratoire de Probabilités, Statistique et Modélisation (FR)
Openalex Percentile: Top 26%
Opportunistic and Delay-Tolerant Networks
0.70
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Increasing paths in random temporal graphs — Gábor Lugosi, Nina Kamčev, et al. · The Annals of Applied Probability (2024) | TGRS Research Map | TGRS