Frequent subgraph discovery

By Michihiro Kuramochi, George Karypis. Proceedings 2001 IEEE International Conference on Data Mining, 2001

1,254 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/7a395ecfbef85ecd9592d4ab4659fab8b45c18ac

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Citations in the corpus

Graph based anomaly detection and description: a survey (2014, background): “Approaches Being a building block for many applications, clustering, and the related, but not identical, problem of community detection, have been studied thoroughly in the data mining and theory communities: METIS (Karypis and Kumar 1995), one of the first partitioning algorithms that were develope”
Mining Frequent Patterns in Data Streams at Multiple Time Granularities (2002, background): “University of New York at Buffalo, jianpei@cse.buffalo. edü IBM T. J. Watson Research Center, psyu@us.ibm.com”
SPIN: mining maximal frequent subgraphs from graph databases (2004, methodology): “Algorithms in the ﬁrst category use a level-wise search scheme based on the Apriori property to enumerate the recurrent subgraphs [12, 13].”
Fg-index: towards verification-free query processing on graph databases (2007, neutral): “Copyright 2007 ACM 978-1-59593-686-8/07/0006 ...$5.00.”
Efficient sampling algorithm for estimating subgraph concentrations and detecting network motifs (2004, background): “Algorithms have been developed for finding frequent subgraphs that recur many times in a set of networks (Inokuchi et al., 2000; Kuramochi and Karypis, 2001).”
Frequent pattern mining: current status and future directions (2007, methodology): “Kuramochi and Karypis (2004) proposed an algorithm, GREW, for finding patterns corresponding to connected”
Graph indexing: a frequent structure-based approach (2004, methodology): “The synthetic data generator was provided by Ku-ramochi et al. [10].”
GAIA: graph classification using evolutionary computation (2010, background): “One straightforward solution [Deshpande, 2005; Bandyopadhyay, 2006] to graph classification is first finding frequent subgraph patterns [Inokuchi, 2000; Kuramochi, 2001; Yan 2002; Huan 2003] in the target graph set and then selecting as features those that rarely occur in the background set.”
Frequent Subtree Mining – An Overview (2004, neutral): “As free tree databases are graph databases, we also include graph mining algorithms in our experimental results; more precisely, we consider the breadth-ﬁrst graph miners FSG [24] and AcGM [21], and the depth-ﬁrst graph miner gSpan [45].”
Frequent substructure-based approaches for classifying chemical compounds (2003, methodology): “FSG, initially presented in [25], with subsequent improvements presented in [27], uses a breadth-first approach to discover the lattice of frequent subgraphs.”
Efficiently mining frequent trees in a forest (2002, methodology): “FSM not only encompasses mining techniques like associations [3] and sequences [4], but it also generalizes to more complex patterns like frequent trees and graphs [12, 14].”
AMIE: association rule mining under incomplete evidence in ontological knowledge bases (2013, background): “Since RDF data can be seen as a graph, mining frequent subtrees [6, 18] is another related field of research.”
Cyclic pattern kernels for predictive graph mining (2004, methodology): “The approach taken there to compute the decomposition of each graph is an iterative one [22].”
Finding Frequent Patterns in a Large Sparse Graph* (2004, methodology): “This candidate generation approach dramatically reduces the number of redundant and non-downward closed patterns that are generated and leads to significant performance improvements over the naive approach (Kuramochi and Karypis, 2001).”
gSpan: graph-based substructure pattern mining (2002, methodology): “Kuramochi and Karypis [5] further developed the idea using adjacent representation of graph and an edge-growing strategy.”