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  Pattern Mining in Frequent Dynamic Subgraphs

Borgwardt, K., Kriegel, H.-P., & Wackersreuther, P. (2006). Pattern Mining in Frequent Dynamic Subgraphs. In Sixth International Conference on Data Mining (ICDM'06) (pp. 818-822). Los Alamitos, CA, USA: IEEE Computer Society.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-CF4D-3 Version Permalink: http://hdl.handle.net/21.11116/0000-0004-968B-6
Genre: Conference Paper

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 Creators:
Borgwardt, KM1, Author              
Kriegel, H-P, Author
Wackersreuther, P, Author
Clifton, CW, Editor
Affiliations:
1External Organizations, ou_persistent22              

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 Abstract: Graph-structured data is becoming increasingly abundant in many application domains. Graph mining aims at finding interesting patterns within this data that represent novel knowledge. While current data mining deals with static graphs that do not change over time, coming years will see the advent of an increasing number of time series of graphs. In this article, we investigate how pattern mining on static graphs can be extended to time series of graphs. In particular, we are considering dynamic graphs with edge insertions and edge deletions over time. We define frequency in this setting and provide algorithmic solutions for finding frequent dynamic subgraph patterns. Existing subgraph mining algorithms can be easily integrated into our framework to make them handle dynamic graphs. Experimental results on real-world data confirm the practical feasibility of our approach.

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 Dates: 2006-12
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1109/ICDM.2006.124
BibTex Citekey: BorgwardtKW2006
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Title: Sixth International Conference on Data Mining (ICDM 2006)
Place of Event: Hong Kong, China
Start-/End Date: 2006-12-18 - 2006-12-22

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Title: Sixth International Conference on Data Mining (ICDM'06)
Source Genre: Proceedings
 Creator(s):
Affiliations:
Publ. Info: Los Alamitos, CA, USA : IEEE Computer Society
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 818 - 822 Identifier: ISBN: 0-7695-2701-9