Fast fully dynamic labelling for distance queries

Farhan, Muhammad; Wang, Qing; Lin, Yu; McKay, Brendan D.

doi:10.1007/s00778-021-00707-z

Cited by 7 publications

(8 citation statements)

References 50 publications

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“…-FulFD: a fully dynamic algorithm proposed in [10] which combines a distance labelling with a graph traversal algorithm to answer distance queries. -FulHL: a fully dynamic labelling algorithm proposed in [11] which combines a highway cover labelling with graph traversal algorithm to answer distance queries. -IncHL + : an online incremental algorithm proposed in [25], which combines a highway cover labelling with a graph traversal algorithm for answering distance queries; -Opt-BiBFS: an online bidirectional BFS algorithm to answer distance queries, using an optimized strategy to expand searches from a direction with less vertices [10].…”

Section: Methodsmentioning

confidence: 99%

“…Fig. 1 Performance overview of our proposed method ParDHL and the state-of-the-art methods DecM [8], DecPLL [9], DecFD [10] and DecHL [11] for processing 1,000 edge deletions.…”

Section: Decm Decpll Decfd Dechl Pardhlmentioning

confidence: 99%

“…A recent method [28] has also attempted to apply a parallel method (PSL) for constructing PLL [20] on dynamic graphs for fast distance computation which unfortunately can only accommodate million-scale graphs. Very recently, Farhan et al [11] proposed a fully dynamic method that leverages the advantages of highway cover distance labelling proposed in [18] for fast processing of dynamic changes on graphs. Nonetheless, their method, similar to previous methods, processes graph updates in the single-update setting which may cause repeated computation w.r.t.…”

Section: Related Workmentioning

confidence: 99%

“…In our work, we consider a distance labelling property based on the notion of highway, i.e., highway cover labelling [18], which has recently been shown to outperform the state-of-the-art methods in the single-update setting [11]. A highway H = (R, δ H ) consists of a set R of landmarks and a distance decoding function δ H : R × R → N + s.t.…”

Section: Problem Formulationmentioning

confidence: 99%

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Efficient Maintenance of Highway Cover Labelling for Distance Queries on Large Dynamic Graphs

Farhan

Wang

2022

Preprint

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Graphs in real-world applications are typically dynamic that undergo rapid changes in their topological structure over time by either adding or deleting edges or vertices. However, it is challenging to design algorithms capable of supporting updates efficiently on dynamic graphs. In this paper, we devise a parallel fully dynamic labelling method to reflect changes on graphs for efficient shortest-path distance computation, a fundamental problem in graph theory. At its core, our solution accelerates query processing through a fully dynamic distance labelling with a limited size but providing a good approximation to bound online searches on dynamic graphs. Our parallel fully dynamic labelling method leverages two sources of efficiency gain: landmark parallelism and anchor parallelism. Furthermore, it can handle both incremental and decremental updates efficiently using a unified search approach and a bounded repairing inference mechanism. We theoretically analyze the correctness, labelling minimality and time complexity of our method, and also conduct extensive experiments to empirically verify its efficiency and scalability on 10 real-world large networks.

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Section: Methodsmentioning

confidence: 99%

“…Fig. 1 Performance overview of our proposed method ParDHL and the state-of-the-art methods DecM [8], DecPLL [9], DecFD [10] and DecHL [11] for processing 1,000 edge deletions.…”

Section: Decm Decpll Decfd Dechl Pardhlmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

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Efficient Maintenance of Highway Cover Labelling for Distance Queries on Large Dynamic Graphs

Farhan

Wang

2022

Preprint

Self Cite

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“…Recently, approaches for dynamic HL were proposed that aim at updating the labels in case of edge cost changes [4,10,17,16]. However, these are designed to deal with the increase or decrease of a single edge weight, while in practice there are often a multitude of non-local edge weight changes to be considered at once.…”

Section: Related Workmentioning

confidence: 99%

Customizable Hub Labeling: Properties and Algorithms

Blum¹,

Storandt²

2022

Preprint

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Hub Labeling (HL) is one of the state-of-the-art preprocessing-based techniques for route planning in road networks. It is a special incarnation of distance labeling, and it is well-studied in both theory and practice. The core concept of HL is to associate a label with each vertex, which consists of a subset of all vertices and respective shortest path information, such that the shortest path distance between any two vertices can be derived from considering the intersection of their labels. HL provides excellent query times but requires a time-consuming preprocessing phase. Therefore, in case of edge cost changes, rerunning the whole preprocessing is not viable. Inspired by the concept of Customizable Route Planning, we hence propose in this paper a Customizable Hub Labeling variant for which the edge costs in the network do not need to be known at construction time. These labels can then be used with any edge costs after conducting a so called customization phase. We study the theoretical properties of Customizable Hub Labelings, provide an O(log 2 n)approximation algorithm for the average label size, and propose efficient customization algorithms.

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Customizable Hub Labeling: Properties and Algorithms

Blum

Storandt

2022

Lecture Notes in Computer Science

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Fast fully dynamic labelling for distance queries

Cited by 7 publications

References 50 publications

Efficient Maintenance of Highway Cover Labelling for Distance Queries on Large Dynamic Graphs

Efficient Maintenance of Highway Cover Labelling for Distance Queries on Large Dynamic Graphs

Customizable Hub Labeling: Properties and Algorithms

Customizable Hub Labeling: Properties and Algorithms

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