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...

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Main Authors: Pierluigi Crescenzi, Clémence Magnien, Andrea Marino
Format: Article
Language:English
Published: MDPI AG 2019-10-01
Series:Algorithms
Subjects:
Online Access:https://www.mdpi.com/1999-4893/12/10/211
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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
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