### 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 → … |

### ASJC Scopus subject areas

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

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## 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