Approximating the Temporal Neighbourhood Function of Large Temporal Graphs
Temporal networks are graphs in which edges have temporal labels, specifying their starting times and their traversal times. Several notions of distances between two nodes in a temporal network can be analyzed, by referring, for example, to the earliest arrival time or to the latest starting time of...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2019-10-01
|
Series: | Algorithms |
Subjects: | |
Online Access: | https://www.mdpi.com/1999-4893/12/10/211 |
id |
doaj-5519bfce056e4e17a46d383f9f5ee553 |
---|---|
record_format |
Article |
spelling |
doaj-5519bfce056e4e17a46d383f9f5ee5532020-11-24T22:09:51ZengMDPI AGAlgorithms1999-48932019-10-01121021110.3390/a12100211a12100211Approximating the Temporal Neighbourhood Function of Large Temporal GraphsPierluigi Crescenzi0Clémence Magnien1Andrea Marino2Institut de Recherche en Informatique Fondamentale, Centre National de la Recherche Scientifique, Université de Paris, F-75013 Paris, FranceCentre National de la Recherche Scientifique, Laboratoire d’Informatique de Paris 6, Sorbonne Université, F-75005 Paris, FranceDipartimento di Statistica, Informatica, Applicazioni “Giuseppe Parenti”, Università degli Studi di Firenze, I-50134 Firenze, ItalyTemporal networks are graphs in which edges have temporal labels, specifying their starting times and their traversal times. Several notions of distances between two nodes in a temporal network can be analyzed, by referring, for example, to the earliest arrival time or to the latest starting time of a temporal path connecting the two nodes. In this paper, we mostly refer to the notion of temporal reachability by using the earliest arrival time. In particular, we first show how the sketch approach, which has already been used in the case of classical graphs, can be applied to the case of temporal networks in order to approximately compute the sizes of the temporal cones of a temporal network. By making use of this approach, we subsequently show how we can approximate the temporal neighborhood function (that is, the number of pairs of nodes reachable from one another in a given time interval) of large temporal networks in a few seconds. Finally, we apply our algorithm in order to analyze and compare the behavior of 25 public transportation temporal networks. Our results can be easily adapted to the case in which we want to refer to the notion of distance based on the latest starting time.https://www.mdpi.com/1999-4893/12/10/211temporal networklink streamtemporal pathearliest arrival timetemporal reachabilityneighborhood functionpublic transportation system |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Pierluigi Crescenzi Clémence Magnien Andrea Marino |
spellingShingle |
Pierluigi Crescenzi Clémence Magnien Andrea Marino Approximating the Temporal Neighbourhood Function of Large Temporal Graphs Algorithms temporal network link stream temporal path earliest arrival time temporal reachability neighborhood function public transportation system |
author_facet |
Pierluigi Crescenzi Clémence Magnien Andrea Marino |
author_sort |
Pierluigi Crescenzi |
title |
Approximating the Temporal Neighbourhood Function of Large Temporal Graphs |
title_short |
Approximating the Temporal Neighbourhood Function of Large Temporal Graphs |
title_full |
Approximating the Temporal Neighbourhood Function of Large Temporal Graphs |
title_fullStr |
Approximating the Temporal Neighbourhood Function of Large Temporal Graphs |
title_full_unstemmed |
Approximating the Temporal Neighbourhood Function of Large Temporal Graphs |
title_sort |
approximating the temporal neighbourhood function of large temporal graphs |
publisher |
MDPI AG |
series |
Algorithms |
issn |
1999-4893 |
publishDate |
2019-10-01 |
description |
Temporal networks are graphs in which edges have temporal labels, specifying their starting times and their traversal times. Several notions of distances between two nodes in a temporal network can be analyzed, by referring, for example, to the earliest arrival time or to the latest starting time of a temporal path connecting the two nodes. In this paper, we mostly refer to the notion of temporal reachability by using the earliest arrival time. In particular, we first show how the sketch approach, which has already been used in the case of classical graphs, can be applied to the case of temporal networks in order to approximately compute the sizes of the temporal cones of a temporal network. By making use of this approach, we subsequently show how we can approximate the temporal neighborhood function (that is, the number of pairs of nodes reachable from one another in a given time interval) of large temporal networks in a few seconds. Finally, we apply our algorithm in order to analyze and compare the behavior of 25 public transportation temporal networks. Our results can be easily adapted to the case in which we want to refer to the notion of distance based on the latest starting time. |
topic |
temporal network link stream temporal path earliest arrival time temporal reachability neighborhood function public transportation system |
url |
https://www.mdpi.com/1999-4893/12/10/211 |
work_keys_str_mv |
AT pierluigicrescenzi approximatingthetemporalneighbourhoodfunctionoflargetemporalgraphs AT clemencemagnien approximatingthetemporalneighbourhoodfunctionoflargetemporalgraphs AT andreamarino approximatingthetemporalneighbourhoodfunctionoflargetemporalgraphs |
_version_ |
1725810392145330176 |