GraphRay: Distributed pathfinder network scaling

Alessio Arleo; Oh Hyun Kwon; Kwan-Liu Ma

doi:10.1109/LDAV.2017.8231853

GraphRay: Distributed pathfinder network scaling

Alessio Arleo, Oh Hyun Kwon, Kwan-Liu Ma

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

Pathfinder network scaling is a graph sparsification technique that has been popularly used due to its efficacy of extracting the 'important' structure of a graph. However, existing algorithms to compute the pathfinder network (PFNET) of a graph have prohibitively expensive time complexity for large graphs: O(n³) for the general case and O(n² log n) for a specific parameter setting, PFNET(r = ∞, q = n - 1), which is considered in many applications. In this paper, we introduce the first distributed technique to compute the pathfinder network with the specific parameters (r = ∞ and q = n - 1) of a large graph with millions of edges. The results of our experiments show our technique is scalable; it efficiently utilizes a parallel distributed computing environment, reducing the running times as more processing units are added.

Original language	English (US)
Title of host publication	2017 IEEE 7th Symposium on Large Data Analysis and Visualization, LDAV 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	74-83
Number of pages	10
Volume	2017-December
ISBN (Electronic)	9781538606179
DOIs	https://doi.org/10.1109/LDAV.2017.8231853
State	Published - Dec 19 2017
Event	7th IEEE Symposium on Large Data Analysis and Visualization, LDAV 2017 - Phoenix, United States Duration: Oct 2 2017 → …

Other

Other	7th IEEE Symposium on Large Data Analysis and Visualization, LDAV 2017
Country	United States
City	Phoenix
Period	10/2/17 → …

ASJC Scopus subject areas

Hardware and Architecture
Information Systems
Media Technology
Library and Information Sciences

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Arleo, A, Kwon, OH & Ma, K-L 2017, GraphRay: Distributed pathfinder network scaling. in 2017 IEEE 7th Symposium on Large Data Analysis and Visualization, LDAV 2017. vol. 2017-December, Institute of Electrical and Electronics Engineers Inc., pp. 74-83, 7th IEEE Symposium on Large Data Analysis and Visualization, LDAV 2017, Phoenix, United States, 10/2/17. https://doi.org/10.1109/LDAV.2017.8231853

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title = "GraphRay: Distributed pathfinder network scaling",

abstract = "Pathfinder network scaling is a graph sparsification technique that has been popularly used due to its efficacy of extracting the 'important' structure of a graph. However, existing algorithms to compute the pathfinder network (PFNET) of a graph have prohibitively expensive time complexity for large graphs: O(n3) for the general case and O(n2 log n) for a specific parameter setting, PFNET(r = ∞, q = n - 1), which is considered in many applications. In this paper, we introduce the first distributed technique to compute the pathfinder network with the specific parameters (r = ∞ and q = n - 1) of a large graph with millions of edges. The results of our experiments show our technique is scalable; it efficiently utilizes a parallel distributed computing environment, reducing the running times as more processing units are added.",

author = "Alessio Arleo and Kwon, {Oh Hyun} and Kwan-Liu Ma",

year = "2017",

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doi = "10.1109/LDAV.2017.8231853",

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booktitle = "2017 IEEE 7th Symposium on Large Data Analysis and Visualization, LDAV 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "7th IEEE Symposium on Large Data Analysis and Visualization, LDAV 2017 ; Conference date: 02-10-2017",

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T2 - 7th IEEE Symposium on Large Data Analysis and Visualization, LDAV 2017

AU - Arleo, Alessio

AU - Kwon, Oh Hyun

AU - Ma, Kwan-Liu

PY - 2017/12/19

Y1 - 2017/12/19

N2 - Pathfinder network scaling is a graph sparsification technique that has been popularly used due to its efficacy of extracting the 'important' structure of a graph. However, existing algorithms to compute the pathfinder network (PFNET) of a graph have prohibitively expensive time complexity for large graphs: O(n3) for the general case and O(n2 log n) for a specific parameter setting, PFNET(r = ∞, q = n - 1), which is considered in many applications. In this paper, we introduce the first distributed technique to compute the pathfinder network with the specific parameters (r = ∞ and q = n - 1) of a large graph with millions of edges. The results of our experiments show our technique is scalable; it efficiently utilizes a parallel distributed computing environment, reducing the running times as more processing units are added.

AB - Pathfinder network scaling is a graph sparsification technique that has been popularly used due to its efficacy of extracting the 'important' structure of a graph. However, existing algorithms to compute the pathfinder network (PFNET) of a graph have prohibitively expensive time complexity for large graphs: O(n3) for the general case and O(n2 log n) for a specific parameter setting, PFNET(r = ∞, q = n - 1), which is considered in many applications. In this paper, we introduce the first distributed technique to compute the pathfinder network with the specific parameters (r = ∞ and q = n - 1) of a large graph with millions of edges. The results of our experiments show our technique is scalable; it efficiently utilizes a parallel distributed computing environment, reducing the running times as more processing units are added.

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BT - 2017 IEEE 7th Symposium on Large Data Analysis and Visualization, LDAV 2017

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 2 October 2017

ER -

GraphRay: Distributed pathfinder network scaling

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