Path problems in temporal graphs

Wu, Huanhuan; Cheng, James; Huang, Silu; Ke, Yiping; Lu, Yi; Xu, Yanyan

doi:10.14778/2732939.2732945

Cited by 217 publications

(178 citation statements)

References 17 publications

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“…Among them, the states of the art include the Connection Scan Algorithm (CSA) [16,38], the Contraction Hierarchies for Timetables (CHT) [21], and T.Patterns [5]. In particular, CSA represents the timetable graph G as two sequences of edges, such that the first (resp.…”

Section: Related Workmentioning

confidence: 99%

Efficient Route Planning on Public Transportation Networks

Wang

Lin

Yang

et al. 2015

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

107

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A public transportation network can often be modeled as a timetable graph where (i) each node represents a station; and (ii) each directed edge u, v is associated with a timetable that records the departure (resp. arrival) time of each vehicle at station u (resp. v). Several techniques have been proposed for various types of route planning on timetable graphs, e.g., retrieving the route from a node to another with the shortest travel time. These techniques, however, either provide insufficient query efficiency or incur significant space overheads.This paper presents Timetable Labelling (TTL), an efficient indexing technique for route planning on timetable graphs. The basic idea of TTL is to associate each node u with a set of labels, each of which records the shortest travel time from u to some other node v given a certain departure time from u; such labels would then be used during query processing to improve efficiency. In addition, we propose query algorithms that enable TTL to support three popular types of route planning queries, and investigate how we reduce the space consumption of TTL with advanced preprocessing and label compression methods. By conducting an extensive set of experiments on real world datasets, we demonstrate that TTL significantly outperforms the states of the art in terms of query efficiency, while incurring moderate preprocessing and space overheads.

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Section: Related Workmentioning

confidence: 99%

Efficient Route Planning on Public Transportation Networks

Wang

Lin

Yang

et al. 2015

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

107

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“…We introduce the terminology and symbols to describe temporal network structure, which basically follow those used in reference [35].…”

Section: Basic Notionsmentioning

confidence: 99%

Coverage centralities for temporal networks

2016

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Abstract. Structure of real networked systems, such as social relationship, can be modeled as temporal networks in which each edge appears only at the prescribed time. Understanding the structure of temporal networks requires quantifying the importance of a temporal vertex, which is a pair of vertex index and time. In this paper, we define two centrality measures of a temporal vertex based on the fastest temporal paths which use the temporal vertex. The definition is free from parameters and robust against the change in time scale on which we focus. In addition, we can efficiently compute these centrality values for all temporal vertices. Using the two centrality measures, we reveal that distributions of these centrality values of realworld temporal networks are heterogeneous. For various datasets, we also demonstrate that a majority of the highly central temporal vertices are located within a narrow time window around a particular time. In other words, there is a bottleneck time at which most information sent in the temporal network passes through a small number of temporal vertices, which suggests an important role of these temporal vertices in spreading phenomena.

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“…Our first set of experiment ensures that the durations in the edges are non-zero in the given temporal graphs. As in [27], the durations are set to 1. The results are shown in Table 2.…”

Section: Efficiency Of the Algorithms For Mst Amentioning

confidence: 99%

“…The first linear time algorithms for computing different kinds of paths in temporal graphs are proposed in [16]. The idea of time ordered edges in [16] is adopted in [27] for a different input format. Note that the algorithm for earliest-arrival paths in [27] has a similar problem as Algorithm 1 with zero durations, and our technique in Algorithm 2 can provide a solution.…”

Section: Related Workmentioning

confidence: 99%

“…The idea of time ordered edges in [16] is adopted in [27] for a different input format. Note that the algorithm for earliest-arrival paths in [27] has a similar problem as Algorithm 1 with zero durations, and our technique in Algorithm 2 can provide a solution. Some recent studies have explored the use of temporal information in different applications.…”

Section: Related Workmentioning

confidence: 99%

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Minimum Spanning Trees in Temporal Graphs

Huang

Liu

2015

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

Self Cite

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The computation of Minimum Spanning Trees (MSTs) is a fundamental graph problem with important applications. However, there has been little study of MSTs for temporal graphs, which is becoming common as time information is collected for many existing networks. We define two types of MSTs for temporal graphs, MST a and MST w , based on the optimization of time and cost, respectively. We propose efficient linear time algorithms for computing MST a. We show that computing MST w is much harder. We design efficient approximation algorithms based on a transformation to the Directed Steiner Tree problem (DST). Our solution also solves the classical DST problem with a better time complexity and the same approximation factor compared to the state-of-the-art algorithm. Our experiments on real temporal networks further verify the effectiveness of our algorithms. For MST w , our solution is capable of shortening the runtime from 10 hours to 3 seconds.

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Path problems in temporal graphs

Cited by 217 publications

References 17 publications

Efficient Route Planning on Public Transportation Networks

Efficient Route Planning on Public Transportation Networks

Coverage centralities for temporal networks

Minimum Spanning Trees in Temporal Graphs

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