### Abstract

Suppose we are given a large graph in which, by some external process, a handful of nodes are marked. What can we say about these nodes? Are they close together in the graph? or, if segregated, how many groups do they form? We approach this problem by trying to find sets of simple connection pathways between sets of marked nodes. We formalize the problem in terms of the Minimum Description Length principle: a pathway is simple when we need only few bits to tell which edges to follow, such that we visit all nodes in a group. Then, the best partitioning is the one that requires the least number of bits to describe the paths that visit all the marked nodes. We prove that solving this problem is NP-hard, and introduce DOT2DOT, an efficient algorithm for partitioning marked nodes by finding simple pathways between nodes. Experimentation shows that DOT2DOT correctly groups nodes for which good connection paths can be constructed, while separating distant nodes.

Original language | English (US) |
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Title of host publication | Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013 |

Editors | Joydeep Ghosh, Zoran Obradovic, Jennifer Dy, Zhi-Hua Zhou, Chandrika Kamath, Srinivasan Parthasarathy |

Publisher | Siam Society |

Pages | 37-45 |

Number of pages | 9 |

ISBN (Electronic) | 9781611972627 |

DOIs | |

State | Published - 2013 |

Event | SIAM International Conference on Data Mining, SDM 2013 - Austin, United States Duration: May 2 2013 → May 4 2013 |

### Publication series

Name | Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013 |
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### Other

Other | SIAM International Conference on Data Mining, SDM 2013 |
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Country | United States |

City | Austin |

Period | 5/2/13 → 5/4/13 |

### ASJC Scopus subject areas

- Computer Science Applications
- Software
- Theoretical Computer Science
- Information Systems
- Signal Processing

## Cite this

*Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013*(pp. 37-45). (Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013). Siam Society. https://doi.org/10.1137/1.9781611972832.5