### Abstract

Many real-world graphs have complex labels on the nodes and edges. Mining only exact patterns yields limited insights, since it may be hard to find exact matches. However, in many domains it is relatively easy to compute some cost (or distance) between different labels. Using this information, it becomes possible to mine a much richer set of approximate subgraph patterns, which preserve the topology but allow bounded label mismatches. We present novel and scalable methods to efficiently solve the approximate isomorphism problem. We show that the mined approximate patterns yield interesting patterns in several real-world graphs ranging from IT and protein interaction networks to protein structures.

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

Title of host publication | HP Laboratories Technical Report |

Edition | 36 |

Publication status | Published - 24 Jun 2013 |

Externally published | Yes |

### Fingerprint

### Keywords

- Approximation techniques
- CMDB
- Data mining
- Graph analysis

### ASJC Scopus subject areas

- Computer Networks and Communications
- Hardware and Architecture
- Software

### Cite this

*HP Laboratories Technical Report*(36 ed.)

**Approximate graph mining with label costs.** / Anchuri, Pranay; Zaki, Mohammed J.; Barkol, Omer; Golan, Shahar; Shamy, Moshe.

Research output: Chapter in Book/Report/Conference proceeding › Chapter

*HP Laboratories Technical Report.*36 edn.

}

TY - CHAP

T1 - Approximate graph mining with label costs

AU - Anchuri, Pranay

AU - Zaki, Mohammed J.

AU - Barkol, Omer

AU - Golan, Shahar

AU - Shamy, Moshe

PY - 2013/6/24

Y1 - 2013/6/24

N2 - Many real-world graphs have complex labels on the nodes and edges. Mining only exact patterns yields limited insights, since it may be hard to find exact matches. However, in many domains it is relatively easy to compute some cost (or distance) between different labels. Using this information, it becomes possible to mine a much richer set of approximate subgraph patterns, which preserve the topology but allow bounded label mismatches. We present novel and scalable methods to efficiently solve the approximate isomorphism problem. We show that the mined approximate patterns yield interesting patterns in several real-world graphs ranging from IT and protein interaction networks to protein structures.

AB - Many real-world graphs have complex labels on the nodes and edges. Mining only exact patterns yields limited insights, since it may be hard to find exact matches. However, in many domains it is relatively easy to compute some cost (or distance) between different labels. Using this information, it becomes possible to mine a much richer set of approximate subgraph patterns, which preserve the topology but allow bounded label mismatches. We present novel and scalable methods to efficiently solve the approximate isomorphism problem. We show that the mined approximate patterns yield interesting patterns in several real-world graphs ranging from IT and protein interaction networks to protein structures.

KW - Approximation techniques

KW - CMDB

KW - Data mining

KW - Graph analysis

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UR - http://www.scopus.com/inward/citedby.url?scp=84879076194&partnerID=8YFLogxK

M3 - Chapter

AN - SCOPUS:84879076194

BT - HP Laboratories Technical Report

ER -