## Abstract

This paper presents two algorithms based on the horizontal and vertical pattern discovery paradigms that find the connected subgraphs that have a sufficient number of edge-disjoint embeddings in a single large undirected labeled sparse graph. These algorithms use three different methods to determine the number of the edge-disjoint embeddings of a subgraph that are based on approximate and exact maximum independent set computations and use it to prune infrequent subgraphs. Experimental evaluation on real datasets from various domains show that both algorithms achieve good performance, scale well to sparse input graphs with more than 100,000 vertices, and significantly outperform a previously developed algorithm.

Original language | English (US) |
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Title of host publication | Proceedings of the Fourth SIAM International Conference on Data Mining |

Editors | M.W. Berry, U. Dayal, C. Kamath, D. Skillicorn |

Pages | 345-356 |

Number of pages | 12 |

State | Published - Jun 22 2004 |

Event | Proceedings of the Fourth SIAM International Conference on Data Mining - Lake Buena Vista, FL, United States Duration: Apr 22 2004 → Apr 24 2004 |

### Other

Other | Proceedings of the Fourth SIAM International Conference on Data Mining |
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Country | United States |

City | Lake Buena Vista, FL |

Period | 4/22/04 → 4/24/04 |

## Keywords

- Frequent subgraph
- Graph mining
- Pattern discovery