### Abstract

Graphlet frequency distribution (GFD) is an analysis tool for understanding the variance of local structure in a graph. Many recent works use GFD for comparing, and characterizing real-life networks. However, the main bottleneck for graph analysis using GFD is the excessive computation cost for obtaining the frequency of each of the graphlets in a large network. To overcome this, we propose a simple, yet powerful algorithm, called GRAFT, that obtains the approximate graphlet frequency for all graphlets that have upto 5 vertices. Comparing to an exact counting algorithm, our algorithm achieves a speedup factor between 10 and 100 for a negligible counting error, which is, on average, less than 5%; For example, exact graphlet counting for ca-AstroPh takes approximately 3 days; but, GRAFT runs for 45 minutes to perform the same task with a counting accuracy of 95.6%.

Original language | English |
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Title of host publication | ACM International Conference Proceeding Series |

Pages | 1467-1471 |

Number of pages | 5 |

DOIs | |

Publication status | Published - 19 Dec 2012 |

Event | 21st ACM International Conference on Information and Knowledge Management, CIKM 2012 - Maui, HI, United States Duration: 29 Oct 2012 → 2 Nov 2012 |

### Other

Other | 21st ACM International Conference on Information and Knowledge Management, CIKM 2012 |
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Country | United States |

City | Maui, HI |

Period | 29/10/12 → 2/11/12 |

### Fingerprint

### Keywords

- approximate graphlet counting
- graph analysis
- graphlet frequency distribution

### ASJC Scopus subject areas

- Human-Computer Interaction
- Computer Networks and Communications
- Computer Vision and Pattern Recognition
- Software

### Cite this

*ACM International Conference Proceeding Series*(pp. 1467-1471) https://doi.org/10.1145/2396761.2398454

**GRAFT : An approximate graphlet counting algorithm for large graph analysis.** / Rahman, Mahmudur; Bhuiyan, Mansurul; Hasan, Mohammad Al.

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

*ACM International Conference Proceeding Series.*pp. 1467-1471, 21st ACM International Conference on Information and Knowledge Management, CIKM 2012, Maui, HI, United States, 29/10/12. https://doi.org/10.1145/2396761.2398454

}

TY - GEN

T1 - GRAFT

T2 - An approximate graphlet counting algorithm for large graph analysis

AU - Rahman, Mahmudur

AU - Bhuiyan, Mansurul

AU - Hasan, Mohammad Al

PY - 2012/12/19

Y1 - 2012/12/19

N2 - Graphlet frequency distribution (GFD) is an analysis tool for understanding the variance of local structure in a graph. Many recent works use GFD for comparing, and characterizing real-life networks. However, the main bottleneck for graph analysis using GFD is the excessive computation cost for obtaining the frequency of each of the graphlets in a large network. To overcome this, we propose a simple, yet powerful algorithm, called GRAFT, that obtains the approximate graphlet frequency for all graphlets that have upto 5 vertices. Comparing to an exact counting algorithm, our algorithm achieves a speedup factor between 10 and 100 for a negligible counting error, which is, on average, less than 5%; For example, exact graphlet counting for ca-AstroPh takes approximately 3 days; but, GRAFT runs for 45 minutes to perform the same task with a counting accuracy of 95.6%.

AB - Graphlet frequency distribution (GFD) is an analysis tool for understanding the variance of local structure in a graph. Many recent works use GFD for comparing, and characterizing real-life networks. However, the main bottleneck for graph analysis using GFD is the excessive computation cost for obtaining the frequency of each of the graphlets in a large network. To overcome this, we propose a simple, yet powerful algorithm, called GRAFT, that obtains the approximate graphlet frequency for all graphlets that have upto 5 vertices. Comparing to an exact counting algorithm, our algorithm achieves a speedup factor between 10 and 100 for a negligible counting error, which is, on average, less than 5%; For example, exact graphlet counting for ca-AstroPh takes approximately 3 days; but, GRAFT runs for 45 minutes to perform the same task with a counting accuracy of 95.6%.

KW - approximate graphlet counting

KW - graph analysis

KW - graphlet frequency distribution

UR - http://www.scopus.com/inward/record.url?scp=84871089313&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84871089313&partnerID=8YFLogxK

U2 - 10.1145/2396761.2398454

DO - 10.1145/2396761.2398454

M3 - Conference contribution

SN - 9781450311564

SP - 1467

EP - 1471

BT - ACM International Conference Proceeding Series

ER -