GREW-A Scalable Frequent Subgraph Discovery Algorithm

Authors:
Michihiro Kuramochi;George Karypis
Affiliations:
University of Minnesota;University of Minnesota
Venue:
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Year:
2004

Citing 0
Cited 10

Discovering informative connection subgraphs in multi-relational graphs

ACM SIGKDD Explorations Newsletter
Maximal Biclique Subgraphs and Closed Pattern Pairs of the Adjacency Matrix: A One-to-One Correspondence and Mining Algorithms

IEEE Transactions on Knowledge and Data Engineering
Optimal candidate generation in spatial co-location mining

Proceedings of the 2009 ACM symposium on Applied Computing
Frequent subgraph discovery in dynamic networks

Proceedings of the Eighth Workshop on Mining and Learning with Graphs
Cl-GBI: a novel approach for extracting typical patterns from graph-structured data

PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Mining dense structures to uncover anomalous behaviour in financial network data

MSM'11 Proceedings of the 2011 international conference on Modeling and Mining Ubiquitous Social Media
Graph-Based Modelling of Concurrent Sequential Patterns

International Journal of Data Warehousing and Mining
A generic topology discovery approach for huge social networks

Proceedings of the 5th ACM COMPUTE Conference: Intelligent & scalable system technologies
A direct mining approach to efficient constrained graph pattern discovery

Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
A new proposal for graph-based image classification using frequent approximate subgraphs

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

Existing algorithms that mine graph datasets to discover patterns corresponding to frequently occurring subgraphs can operate efficiently on graphs that are sparse, contain a large number of relatively small connected components, have vertices with low and bounded degrees, and contain well-labeled vertices and edges. However, for graphs that do not share these characteristics, these algorithms become highly unscalable. In this paper we present a heuristic algorithm called GREW to overcome the limitations of existing complete or heuristic frequent subgraph discovery algorithms. GREW is designed to operate on a large graph and to find patterns corresponding to connected subgraphs that have a large number of vertex-disjoint embeddings. Our experimental evaluation shows that GREW is efficient, can scale to very large graphs, and find non-trivial patterns.