English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Frequent subgraph discovery in dynamic networks

Wackersreuther, B., Wackersreuther, P., Oswald, A., Böhm, C., & Borgwardt, K. (2010). Frequent subgraph discovery in dynamic networks. In MLG '10: Proceedings of the Eighth Workshop on Mining and Learning with Graphs (pp. 155-162). New York, NY, USA: ACM Press.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/21.11116/0000-0002-814E-5 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-814F-4
Genre: Conference Paper

Files

show Files

Locators

show
hide
Locator:
https://dl.acm.org/citation.cfm?id=1830272 (Publisher version)
Description:
-

Creators

show
hide
 Creators:
Wackersreuther, B, Author
Wackersreuther, P, Author
Oswald, A, Author
Böhm, C, Author
Borgwardt, KM1, 2, Author              
Affiliations:
1Former Research Group Machine Learning and Computational Biology, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_2528696              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

Content

show
hide
Free keywords: -
 Abstract: In many application domains, graphs are utilized to model entities and their relationships, and graph mining is important to detect patterns within these relationships. While the majority of recent data mining techniques deal with static graphs that do not change over time, recent years have witnessed the advent of an increasing number of time series of graphs. In this paper, we define a novel framework to perform frequent subgraph discovery in dynamic networks. In particular, we are considering dynamic graphs with edge insertions and edge deletions over time. Existing subgraph mining algorithms can be easily integrated into our framework to make them handle dynamic graphs. Finally, an extensive experimental evaluation on a large real-world case study confirms the practical feasibility of our approach.

Details

show
hide
Language(s):
 Dates: 2010-07
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1145/1830252.1830272
 Degree: -

Event

show
hide
Title: Eighth Workshop on Mining and Learning with Graphs (MLG '10)
Place of Event: Washington, DC, USA
Start-/End Date: 2010-07-24 - 2010-07-25

Legal Case

show

Project information

show

Source 1

show
hide
Title: MLG '10: Proceedings of the Eighth Workshop on Mining and Learning with Graphs
Source Genre: Proceedings
 Creator(s):
Affiliations:
Publ. Info: New York, NY, USA : ACM Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 155 - 162 Identifier: ISBN: 978-1-4503-0214-2