An efficient algorithm for discovering frequent subgraphs

By Michihiro Kuramochi, George Karypis. IEEE Transactions on Knowledge and Data Engineering, 2004

413 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/77259f9b60901545f6f882d682870bb0aa067566

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Citations in the corpus

Efficiently mining frequent trees in a forest: algorithms and applications (2005, background): “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 [18], [21].”
Frequent substructure-based approaches for classifying chemical compounds (2003, methodology): “It uses a sophisticated scheme for candidate generation [25] that mi nimizes the number of times each candidate subgraph gets generated and also dramatically reduces the generation of s ubgraphs that fail the downward closure test.”
Finding Frequent Patterns in a Large Sparse Graph* (2004, methodology): “labels for fast processingand various vertex invariants to reducethe complexity of determiningthe canonicallabel of a graph[45, 46].”
Comparison of descriptor spaces for chemical compound retrieval and classification (2006, methodology): “To generate frequent sub-structures, we use the FSG algorithm described in [ 32 ], although any other frequent subgraph discovery algorithm could be used.”
Patterns of Influence in a Recommendation Network (2006, background): “This is an instance of the general issue of frequent subgraph mining [8, 11, 17]; however, most of the prior work in this area is focused on graphs that are richly labeled and undirected, often motivated by applications to chemical compound and bioinformatics datasets.”
Frequent sub-structure-based approaches for classifying chemical compounds (2005, background): “FSG, initially presented in [18], with subsequent improvements presented in [20], uses a breadth-first approach to discover the lattice of frequent subgraphs.”
A survey of frequent subgraph mining algorithms (2012, methodology): “Experimental evaluation was performed using a chemical database with more than 20,000 chemical compounds to show that gFSG operated well with low support values and scaled linearly with respect to data size.”
Frequent Subtree Mining – An Overview (2004, neutral): “the implementations of some graph miners are much older than those of the tree miners and have been repeatedly optimized in recent years [24, 25 ].”
Fast Categorization of Web Documents Represented by Graphs (2006, methodology): “This fact makes our problem much easier than the standard subgraph discovery case [ 12 ] where such restriction does not exist.”