A Shared-Memory Algorithm for Updating Tree-Based Properties of Large Dynamic Networks

This paper presents a network-based template for analyzing large-scale dynamic data. Specifically, we propose a novel shared-memory parallel algorithm for updating tree-based structures or properties, such as connected components (CC) and minimum spanning trees (MST), on dynamic networks. The underl...

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Veröffentlicht in:IEEE transactions on big data Jg. 8; H. 2; S. 302 - 317
Hauptverfasser: Srinivasan, Sriram, Pollard, Samuel D., Norris, Boyana, Das, Sajal K., Bhowmick, Sanjukta
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.04.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Big Data
connected components
Data structures
Dynamic networks
Graph theory
Heuristic algorithms
Image edge detection
Maintenance engineering
minimum spanning tree
Networks
Parallel algorithms
Partitioning algorithms
shared memory algorithms
ISSN:2332-7790, 2372-2096
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Abstract This paper presents a network-based template for analyzing large-scale dynamic data. Specifically, we propose a novel shared-memory parallel algorithm for updating tree-based structures or properties, such as connected components (CC) and minimum spanning trees (MST), on dynamic networks. The underlying idea is to update the information in a rooted tree data structure that stores the edges of the network that are most relevant to the analysis. Extensive experiments on real-world and synthetic networks demonstrate that, with the exception of the inherently sequential component for creating the rooted tree, our proposed updatiing algorithm is scalable and, in most cases, also requires significantly less memory, energy, and time than recomputing-from-scratch algorithm. To the best of our knowledge, this is the first parallel algorithm for updating MST on weighted dynamic networks. The rooted-tree based framework that we propose in this paper can be extended for updating other weighted and unweighted tree-based properties such as single source shortest path and betweenness and closeness centrality.
AbstractList This paper presents a network-based template for analyzing large-scale dynamic data. Specifically, we propose a novel shared-memory parallel algorithm for updating tree-based structures or properties, such as connected components (CC) and minimum spanning trees (MST), on dynamic networks. The underlying idea is to update the information in a rooted tree data structure that stores the edges of the network that are most relevant to the analysis. Extensive experiments on real-world and synthetic networks demonstrate that, with the exception of the inherently sequential component for creating the rooted tree, our proposed updatiing algorithm is scalable and, in most cases, also requires significantly less memory, energy, and time than recomputing-from-scratch algorithm. To the best of our knowledge, this is the first parallel algorithm for updating MST on weighted dynamic networks. The rooted-tree based framework that we propose in this paper can be extended for updating other weighted and unweighted tree-based properties such as single source shortest path and betweenness and closeness centrality.
Author Pollard, Samuel D.
Das, Sajal K.
Norris, Boyana
Bhowmick, Sanjukta
Srinivasan, Sriram
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SubjectTerms Algorithms
Big Data
connected components
Data structures
Dynamic networks
Graph theory
Heuristic algorithms
Image edge detection
Maintenance engineering
minimum spanning tree
Networks
Parallel algorithms
Partitioning algorithms
shared memory algorithms
Title A Shared-Memory Algorithm for Updating Tree-Based Properties of Large Dynamic Networks
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