### Abstract

Graph data mining is defined as searching in an input graph for all subgraphs that satisfy some property that makes them interesting to the user. Examples of graph data mining problems include frequent subgraph mining, counting motifs, and enumerating cliques. These problems differ from other graph processing problems such as PageRank or shortest path in that graph data mining requires searching through an exponential number of subgraphs. Most current parallel graph analytics systems do not provide good support for graph data mining. One notable exception is Arabesque, a system that was built specifically to support graph data mining. Arabesque provides a simple programming model to express graph data mining computations, and a highly scalable and efficient implementation of this model, scaling to billions of subgraphs on hundreds of cores. This demonstration will showcase the Arabesque system, focusing on the end-user experience and showing how Arabesque can be used to simply and efficiently solve practical graph data mining problems that would be difficult with other systems.

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

Title of host publication | SIGMOD 2017 - Proceedings of the 2017 ACM International Conference on Management of Data |

Publisher | Association for Computing Machinery |

Pages | 1647-1650 |

Number of pages | 4 |

Volume | Part F127746 |

ISBN (Electronic) | 9781450341974 |

DOIs | |

Publication status | Published - 9 May 2017 |

Event | 2017 ACM SIGMOD International Conference on Management of Data, SIGMOD 2017 - Chicago, United States Duration: 14 May 2017 → 19 May 2017 |

### Other

Other | 2017 ACM SIGMOD International Conference on Management of Data, SIGMOD 2017 |
---|---|

Country | United States |

City | Chicago |

Period | 14/5/17 → 19/5/17 |

### Fingerprint

### ASJC Scopus subject areas

- Software
- Information Systems

### Cite this

*SIGMOD 2017 - Proceedings of the 2017 ACM International Conference on Management of Data*(Vol. Part F127746, pp. 1647-1650). Association for Computing Machinery. https://doi.org/10.1145/3035918.3058742