### 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(n^{3}) for the general case and O(n^{2} 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) |
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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 |
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Country | United States |

City | Phoenix |

Period | 10/2/17 → … |

### Fingerprint

### ASJC Scopus subject areas

- Hardware and Architecture
- Information Systems
- Media Technology
- Library and Information Sciences

### Cite this

*2017 IEEE 7th Symposium on Large Data Analysis and Visualization, LDAV 2017*(Vol. 2017-December, pp. 74-83). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/LDAV.2017.8231853

**GraphRay : Distributed pathfinder network scaling.** / Arleo, Alessio; Kwon, Oh Hyun; Ma, Kwan-Liu.

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

*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

}

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.

UR - http://www.scopus.com/inward/record.url?scp=85048406744&partnerID=8YFLogxK

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 -