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Pattern Mining in Frequent Dynamic Subgraphs


Conference Paper


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.

Author(s): Borgwardt, KM. and Kriegel, H-P. and Wackersreuther, P.
Pages: 818-822
Year: 2006
Month: December
Day: 0
Editors: Clifton, C.W.
Publisher: IEEE Computer Society

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1109/ICDM.2006.124
Event Name: Sixth International Conference on Data Mining (ICDM 2006)
Event Place: Hong Kong

Address: Los Alamitos, CA, USA
Digital: 0
ISBN: 0-7695-2701-9

Links: Web


  title = {Pattern Mining in Frequent Dynamic Subgraphs},
  author = {Borgwardt, KM. and Kriegel, H-P. and Wackersreuther, P.},
  pages = {818-822},
  editors = {Clifton, C.W.},
  publisher = {IEEE Computer Society},
  address = {Los Alamitos, CA, USA},
  month = dec,
  year = {2006},
  month_numeric = {12}