### Abstract

Original language | English |
---|---|

Title of host publication | 2018 IEEE International Conference on Big Data (Big Data) |

Publisher | IEEE Institute of Electrical and Electronic Engineers |

Pages | 3784-3792 |

ISBN (Electronic) | 978-1-5386-5035-6 |

ISBN (Print) | 978-1-5386-5036-3, 978-1-5386-5034-9 |

DOIs | |

Publication status | Published - 22 Jan 2019 |

MoE publication type | A4 Article in a conference publication |

Event | Advances in High Dimensional Big Data: Workshop in conjunction with the 2018 IEEE International Conference on Big Data (IEEE Big Data 2018) - Seattle, United States Duration: 10 Dec 2018 → 13 Dec 2018 |

### Workshop

Workshop | Advances in High Dimensional Big Data |
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Country | United States |

City | Seattle |

Period | 10/12/18 → 13/12/18 |

### Fingerprint

### Keywords

- graph theory
- statistical analysis
- Big Data
- peer-to-peer computing
- partitioning
- approximation algorithms
- stochastic processes

### Cite this

*2018 IEEE International Conference on Big Data (Big Data)*(pp. 3784-3792). [8622118] IEEE Institute of Electrical and Electronic Engineers . https://doi.org/10.1109/BigData.2018.8622118

}

*2018 IEEE International Conference on Big Data (Big Data).*, 8622118, IEEE Institute of Electrical and Electronic Engineers , pp. 3784-3792, Advances in High Dimensional Big Data, Seattle, United States, 10/12/18. https://doi.org/10.1109/BigData.2018.8622118

**Analysis of large sparse graphs using regular decomposition of graph distance matrices.** / Reittu, Hannu; Leskelä, Lasse; Fiorucci, Marco; Räty, Tomi.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Analysis of large sparse graphs using regular decomposition of graph distance matrices

AU - Reittu, Hannu

AU - Leskelä, Lasse

AU - Fiorucci, Marco

AU - Räty, Tomi

PY - 2019/1/22

Y1 - 2019/1/22

N2 - Statistical analysis of large and sparse graphs is a challenging problem in data science due to the high dimensionality and nonlinearity of the problem. This paper presents a fast and scalable algorithm for partitioning such graphs into disjoint groups based on observed graph distances from a set of reference nodes. The resulting partition provides a low-dimensional approximation of the full distance matrix which helps to reveal global structural properties of the graph using only small samples of the distance matrix. The presented algorithm is inspired by the information-theoretic minimum description principle. We investigate the performance of this algorithm for selected real data sets and for synthetic graph data sets generated using stochastic block models and power-law random graphs, together with analytical considerations for sparse stochastic block models with bounded average degrees.

AB - Statistical analysis of large and sparse graphs is a challenging problem in data science due to the high dimensionality and nonlinearity of the problem. This paper presents a fast and scalable algorithm for partitioning such graphs into disjoint groups based on observed graph distances from a set of reference nodes. The resulting partition provides a low-dimensional approximation of the full distance matrix which helps to reveal global structural properties of the graph using only small samples of the distance matrix. The presented algorithm is inspired by the information-theoretic minimum description principle. We investigate the performance of this algorithm for selected real data sets and for synthetic graph data sets generated using stochastic block models and power-law random graphs, together with analytical considerations for sparse stochastic block models with bounded average degrees.

KW - graph theory

KW - statistical analysis

KW - Big Data

KW - peer-to-peer computing

KW - partitioning

KW - approximation algorithms

KW - stochastic processes

UR - http://www.scopus.com/inward/record.url?scp=85062642513&partnerID=8YFLogxK

U2 - 10.1109/BigData.2018.8622118

DO - 10.1109/BigData.2018.8622118

M3 - Conference article in proceedings

SN - 978-1-5386-5036-3

SN - 978-1-5386-5034-9

SP - 3784

EP - 3792

BT - 2018 IEEE International Conference on Big Data (Big Data)

PB - IEEE Institute of Electrical and Electronic Engineers

ER -