Frequent subgraph discovery in dynamic networks

Authors:
Bianca Wackersreuther;Peter Wackersreuther;Annahita Oswald;Christian Böhm;Karsten M. Borgwardt
Affiliations:
University of Munich, Munich, Germany;University of Munich, Munich, Germany;University of Munich, Munich, Germany;University of Munich, Munich, Germany;MPIs for Developmental Biology and Biological Cybernetics, Tübingen, Germany
Venue:
Proceedings of the Eighth Workshop on Mining and Learning with Graphs
Year:
2010

Citing 11
Cited 0

Computers and Intractability: A Guide to the Theory of NP-Completeness

Computers and Intractability: A Guide to the Theory of NP-Completeness
Complete Mining of Frequent Patterns from Graphs: Mining Graph Data

Machine Learning
Frequent Subgraph Discovery

ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Color Set Size Problem with Application to String Matching

CPM '92 Proceedings of the Third Annual Symposium on Combinatorial Pattern Matching
gSpan: Graph-Based Substructure Pattern Mining

ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
GREW-A Scalable Frequent Subgraph Discovery Algorithm

ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Graphs over time: densification laws, shrinking diameters and possible explanations

Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Efficient sampling algorithm for estimating subgraph concentrations and detecting network motifs

Bioinformatics
Efficient Detection of Network Motifs

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Pattern Mining in Frequent Dynamic Subgraphs

ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Substructure discovery using minimum description length and background knowledge

Journal of Artificial Intelligence Research

Quantified Score

Hi-index	0.00

Visualization

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.