ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
The complexity of theorem-proving procedures
STOC '71 Proceedings of the third annual ACM symposium on Theory of computing
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Closure-Tree: An Index Structure for Graph Queries
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Graph mining: Laws, generators, and algorithms
ACM Computing Surveys (CSUR)
FANMOD: a tool for fast network motif detection
Bioinformatics
Efficient Detection of Network Motifs
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Identifying bug signatures using discriminative graph mining
Proceedings of the eighteenth international symposium on Software testing and analysis
Strategies for Network Motifs Discovery
E-SCIENCE '09 Proceedings of the 2009 Fifth IEEE International Conference on e-Science
Network motif discovery using subgraph enumeration and symmetry-breaking
RECOMB'07 Proceedings of the 11th annual international conference on Research in computational molecular biology
g-tries: an efficient data structure for discovering network motifs
Proceedings of the 2010 ACM Symposium on Applied Computing
Efficient subgraph frequency estimation with g-tries
WABI'10 Proceedings of the 10th international conference on Algorithms in bioinformatics
Enhancing graph database indexing by suffix tree structure
PRIB'10 Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics
Efficient Parallel Subgraph Counting Using G-Tries
CLUSTER '10 Proceedings of the 2010 IEEE International Conference on Cluster Computing
Characterizing Wikipedia pages using edit network motif profiles
Proceedings of the 3rd international workshop on Search and mining user-generated contents
Towards a faster network-centric subgraph census
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
G-Tries: a data structure for storing and finding subgraphs
Data Mining and Knowledge Discovery
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In this paper we present an universal methodology for finding all the occurrences of a given set of subgraphs in one single larger graph. Past approaches would either enumerate all possible subgraphs of a certain size or query a single subgraph. We use g-tries, a data structure specialized in dealing with subgraph sets. G-Tries store the topological information on a tree that exposes common substructure. Using a specialized canonical form and symmetry breaking conditions, a single non-redundant search of the entire set of subgraphs is possible. We give results of applying g-tries querying to different social networks, showing that we can efficiently find the occurrences of a set containing subgraphs of multiple sizes, outperforming previous methods.