Pattern mining in frequent dynamic subgraphs

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Pattern mining in frequent dynamic subgraphs (2006)

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by Karsten M. Borgwardt , Hans-Peter Kriegel , Peter Wackersreuther

Venue:	IN ICDM
Citations:	36 - 2 self

BibTeX

@INPROCEEDINGS{Borgwardt06patternmining,
    author = {Karsten M. Borgwardt and Hans-Peter Kriegel and Peter Wackersreuther},
    title = {Pattern mining in frequent dynamic subgraphs},
    booktitle = {IN ICDM},
    year = {2006},
    pages = {818--822},
    publisher = {}
}

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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.

Keyphrases

pattern mining frequent dynamic subgraphs dynamic graph time series static graph graph-structured data algorithmic solution many application domain subgraph mining algorithm graph mining aim real-world data edge insertion current data mining deal novel knowledge experimental result edge deletion frequent dynamic subgraph pattern practical feasibility

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