Sixth International Conference on Data Mining (ICDM'06) (2006)
Dec. 18, 2006 to Dec. 22, 2006
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2006.124
Karsten M. Borgwardt , Ludwig-Maximilians-Universitat, Germany
Hans-Peter Kriegel , Ludwig-Maximilians-Universitat, Germany
Peter Wackersreuther , Ludwig-Maximilians-Universitat, Germany
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.
H. Kriegel, K. M. Borgwardt and P. Wackersreuther, "Pattern Mining in Frequent Dynamic Subgraphs," Sixth International Conference on Data Mining (ICDM'06)(ICDM), Hong Kong, 2006, pp. 818-822.