A Fully Dynamic Algorithm for Distributed Shortest Paths

A fully dynamic algorithm for distributed shortest paths

Theoretical Computer Science ◽

10.1016/s0304-3975(02)00619-9 ◽

2003 ◽

Vol 297 (1-3) ◽

pp. 83-102 ◽

Cited By ~ 20

Author(s):

Serafino Cicerone ◽

Gabriele Di Stefano ◽

Daniele Frigioni ◽

Umberto Nanni

Keyword(s):

Shortest Paths ◽

Dynamic Algorithm

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An efficient dynamic algorithm for maintaining all-pairs shortest paths in stochastic networks

IEEE Transactions on Computers ◽

10.1109/tc.2006.83 ◽

2006 ◽

Vol 55 (6) ◽

pp. 686-702 ◽

Cited By ~ 25

Author(s):

S. Misra ◽

B.J. Oommen

Keyword(s):

Shortest Paths ◽

Stochastic Networks ◽

Dynamic Algorithm ◽

All Pairs Shortest Paths

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Faster Algorithms for Mining Shortest-Path Distances from Massive Time-Evolving Graphs

Algorithms ◽

10.3390/a13080191 ◽

2020 ◽

Vol 13 (8) ◽

pp. 191

Author(s):

Mattia D’Emidio

Keyword(s):

Shortest Path ◽

Real World ◽

Large Scale ◽

Web Search ◽

Shortest Paths ◽

Route Planning ◽

Compact Representation ◽

Worst Case ◽

Evolving Graphs ◽

Dynamic Algorithm

Computing shortest-path distances is a fundamental primitive in the context of graph data mining, since this kind of information is essential in a broad range of prominent applications, which include social network analysis, data routing, web search optimization, database design and route planning. Standard algorithms for shortest paths (e.g., Dijkstra’s) do not scale well with the graph size, as they take more than a second or huge memory overheads to answer a single query on the distance for large-scale graph datasets. Hence, they are not suited to mine distances from big graphs, which are becoming the norm in most modern application contexts. Therefore, to achieve faster query answering, smarter and more scalable methods have been designed, the most effective of them based on precomputing and querying a compact representation of the transitive closure of the input graph, called the 2-hop-cover labeling. To use such approaches in realistic time-evolving scenarios, when the managed graph undergoes topological modifications over time, specific dynamic algorithms, carefully updating the labeling as the graph evolves, have been introduced. In fact, recomputing from scratch the 2-hop-cover structure every time the graph changes is not an option, as it induces unsustainable time overheads. While the state-of-the-art dynamic algorithm to update a 2-hop-cover labeling against incremental modifications (insertions of arcs/vertices, arc weights decreases) offers very fast update times, the only known solution for decremental modifications (deletions of arcs/vertices, arc weights increases) is still far from being considered practical, as it requires up to tens of seconds of processing per update in several prominent classes of real-world inputs, as experimentation shows. In this paper, we introduce a new dynamic algorithm to update 2-hop-cover labelings against decremental changes. We prove its correctness, formally analyze its worst-case performance, and assess its effectiveness through an experimental evaluation employing both real-world and synthetic inputs. Our results show that it improves, by up to several orders of magnitude, upon average update times of the only existing decremental algorithm, thus representing a step forward towards real-time distance mining in general, massive time-evolving graphs.

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A New Fully Dynamic Algorithm for Distributed Shortest Paths and Its Experimental Evaluation

Experimental Algorithms - Lecture Notes in Computer Science ◽

10.1007/978-3-642-13193-6_6 ◽

2010 ◽

pp. 59-70 ◽

Cited By ~ 5

Author(s):

Serafino Cicerone ◽

Gianlorenzo D’Angelo ◽

Gabriele Di Stefano ◽

Daniele Frigioni ◽

Vinicio Maurizio

Keyword(s):

Experimental Evaluation ◽

Shortest Paths ◽

Dynamic Algorithm

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Experimental Evaluations of Dynamic Algorithm for Maintaining Shortest-Paths Trees on Real-World Networks

Interdisciplinary Information Sciences ◽

10.4036/iis.2015.25 ◽

2015 ◽

Vol 21 (1) ◽

pp. 25-35

Author(s):

Takashi HASEGAWA ◽

Takehiro ITO ◽

Akira SUZUKI ◽

Xiao ZHOU

Keyword(s):

Real World ◽

Shortest Paths ◽

Dynamic Algorithm

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eMap: A Web Application for Identifying and Visualizing Electron or Hole Hopping Pathways in Proteins

10.26434/chemrxiv.7889993 ◽

2019 ◽

Author(s):

Ruslan N. Tazhigulov ◽

James R. Gayvert ◽

Melissa Wei ◽

Ksenia B. Bravaya

Keyword(s):

Web Application ◽

Shortest Paths ◽

Coarse Grained ◽

Decay Parameter ◽

Electron Hole ◽

Web Based ◽

Aromatic Amino Acid Residue ◽

Pathways Model ◽

Visualization Tools ◽

Effective Decay

<p>eMap is a web-based platform for identifying and visualizing electron or hole transfer pathways in proteins based on their crystal structures. The underlying model can be viewed as a coarse-grained version of the Pathways model, where each tunneling step between hopping sites represented by electron transfer active (ETA) moieties is described with one eﬀective decay parameter that describes protein-mediated tunneling. ETA moieties include aromatic amino acid residue side chains and aromatic fragments of cofactors that are automatically detected, and, in addition, electron/hole residing sites that can be speciﬁed by the users. The software searches for the shortest paths connecting the user-speciﬁed electron/hole source to either all surface-exposed ETA residues or to the user-speciﬁed target. The identiﬁed pathways are ranked based on their length. The pathways are visualized in 2D as a graph, in which each node represents an ETA site, and in 3D using available protein visualization tools. Here, we present the capability and user interface of eMap 1.0, which is available at https://emap.bu.edu.</p>

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Computer algorithms

10.1093/oso/9780198805090.003.0008 ◽

2018 ◽

Author(s):

Mark Newman

Keyword(s):

Data Structures ◽

Analysis Of Algorithms ◽

Shortest Paths ◽

Minimum Cut ◽

Network Data ◽

Computer Algorithms ◽

Breadth First Search ◽

Augmenting Path ◽

Basic Concepts ◽

Maximum Flows

This chapter introduces some of the fundamental concepts of numerical network calculations. The chapter starts with a discussion of basic concepts of computational complexity and data structures for storing network data, then progresses to the description and analysis of algorithms for a range of network calculations: breadth-first search and its use for calculating shortest paths, shortest distances, components, closeness, and betweenness; Dijkstra's algorithm for shortest paths and distances on weighted networks; and the augmenting path algorithm for calculating maximum flows, minimum cut sets, and independent paths in networks.

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