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 | |
| 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 → … |
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ASJC Scopus subject areas
- Hardware and Architecture
- Information Systems
- Media Technology
- Library and Information Sciences
Cite this
GraphRay : Distributed pathfinder network scaling. / Arleo, Alessio; Kwon, Oh Hyun; Ma, Kwan-Liu.
2017 IEEE 7th Symposium on Large Data Analysis and Visualization, LDAV 2017. Vol. 2017-December Institute of Electrical and Electronics Engineers Inc., 2017. p. 74-83.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - GraphRay
T2 - Distributed pathfinder network scaling
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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UR - http://www.scopus.com/inward/citedby.url?scp=85048406744&partnerID=8YFLogxK
U2 - 10.1109/LDAV.2017.8231853
DO - 10.1109/LDAV.2017.8231853
M3 - Conference contribution
VL - 2017-December
SP - 74
EP - 83
BT - 2017 IEEE 7th Symposium on Large Data Analysis and Visualization, LDAV 2017
PB - Institute of Electrical and Electronics Engineers Inc.
ER -