### Abstract

We propose a novel distributed algorithm for mining frequent subgraphs from a single, very large, labeled network. Our approach is the first distributed method to mine a massive input graph that is too large to fit in the memory of any individual compute node. The input graph thus has to be partitioned among the nodes, which can lead to potential false negatives. Furthermore, for scalable performance it is crucial to minimize the communication among the compute nodes. Our algorithm, DistGraph, ensures that there are no false negatives, and uses a set of optimizations and efficient collective communication operations to minimize information exchange. To our knowledge DistGraph is the first approach demonstrated to scale to graphs with over a billion vertices and edges. Scalability results on up to 2048 IBM Blue Gene/Q compute nodes, with 16 cores each, show very good speedup.

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

Pages (from-to) | 1024-1052 |

Number of pages | 29 |

Journal | Data Mining and Knowledge Discovery |

Volume | 30 |

Issue number | 5 |

DOIs | |

Publication status | Published - 1 Sep 2016 |

Externally published | Yes |

### Fingerprint

### Keywords

- Distributed graph mining
- Frequent subgraph mining
- High performance computing
- Parallel graph mining
- Single large graph

### ASJC Scopus subject areas

- Information Systems
- Computer Science Applications
- Computer Networks and Communications

### Cite this

*Data Mining and Knowledge Discovery*,

*30*(5), 1024-1052. https://doi.org/10.1007/s10618-016-0466-x

**A distributed approach for graph mining in massive networks.** / Talukder, N.; Zaki, M. J.

Research output: Contribution to journal › Article

*Data Mining and Knowledge Discovery*, vol. 30, no. 5, pp. 1024-1052. https://doi.org/10.1007/s10618-016-0466-x

}

TY - JOUR

T1 - A distributed approach for graph mining in massive networks

AU - Talukder, N.

AU - Zaki, M. J.

PY - 2016/9/1

Y1 - 2016/9/1

N2 - We propose a novel distributed algorithm for mining frequent subgraphs from a single, very large, labeled network. Our approach is the first distributed method to mine a massive input graph that is too large to fit in the memory of any individual compute node. The input graph thus has to be partitioned among the nodes, which can lead to potential false negatives. Furthermore, for scalable performance it is crucial to minimize the communication among the compute nodes. Our algorithm, DistGraph, ensures that there are no false negatives, and uses a set of optimizations and efficient collective communication operations to minimize information exchange. To our knowledge DistGraph is the first approach demonstrated to scale to graphs with over a billion vertices and edges. Scalability results on up to 2048 IBM Blue Gene/Q compute nodes, with 16 cores each, show very good speedup.

AB - We propose a novel distributed algorithm for mining frequent subgraphs from a single, very large, labeled network. Our approach is the first distributed method to mine a massive input graph that is too large to fit in the memory of any individual compute node. The input graph thus has to be partitioned among the nodes, which can lead to potential false negatives. Furthermore, for scalable performance it is crucial to minimize the communication among the compute nodes. Our algorithm, DistGraph, ensures that there are no false negatives, and uses a set of optimizations and efficient collective communication operations to minimize information exchange. To our knowledge DistGraph is the first approach demonstrated to scale to graphs with over a billion vertices and edges. Scalability results on up to 2048 IBM Blue Gene/Q compute nodes, with 16 cores each, show very good speedup.

KW - Distributed graph mining

KW - Frequent subgraph mining

KW - High performance computing

KW - Parallel graph mining

KW - Single large graph

UR - http://www.scopus.com/inward/record.url?scp=84973659265&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84973659265&partnerID=8YFLogxK

U2 - 10.1007/s10618-016-0466-x

DO - 10.1007/s10618-016-0466-x

M3 - Article

VL - 30

SP - 1024

EP - 1052

JO - Data Mining and Knowledge Discovery

JF - Data Mining and Knowledge Discovery

SN - 1384-5810

IS - 5

ER -