An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data

By Akihiro Inokuchi, Takashi Washio, Hiroshi Motoda. European Conference on Principles of Data Mining and Knowledge Discovery, 2000

1,185 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Information Systems

View Paper: https://www.semanticscholar.org/paper/c708bc122f789e0c54a3350e149cac44a477f7ba

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In Computational Theory and Mathematics

In Information Systems

Citations in the corpus

TrGraph: Cross-Network Transfer Learning via Common Signature Subgraphs (2015, methodology): “In this work, we also adopt the idea of graph transfer but focus on non-streaming scenarios for cross-network node classification.”
Frequent subgraph discovery (2001, methodology): “AGM, a frequent induced subgraph discovery algorithm, required about 8 days for 10% and 40 minutes for 20% with almost the same dataset on 400MHz PC [14].”
Frequent substructure-based approaches for classifying chemical compounds (2003, methodology): “These are the AGM algorithm developed by Inokuch i et al [18], the FSG algorithm developed by members of our group [23], the chemical sub-structure discovery alg orithm developed by Borgelt and Berthold [3], and the gSpan algorithm developed by Yan and Han [43].”
Frequent sub-structure-based approaches for classifying chemical compounds (2005, neutral): “The first class builds a classification model using a set of physico-chemical properties derived from the compounds structure, called quantitative structure-activity relationships (QSAR) [10, 11, 1], whereas the second class operates directly on the structure of the chemical compound and tries to au”
Efficiently mining frequent trees in a forest (2002, 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 [12, 14].”
GADDI: distance index based subgraph matching in biological networks (2009, background): “As a result, graph database research has attracted a large amount of attention from the database and data mining communities, such as subgraph search in a database of multiple graphs [5, 23, 38, 46, 51, 52, 53, 16], approximate subgraph matching [17, 40, 44, 47, 48, 11], frequent subgraph mining [21”
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).”
A survey of frequent subgraph mining algorithms (2012, methodology): “> AGM (Inokuchi et al., 2000) is a well-established algorithm used to identify frequent induced subgraphs.”
Frequent pattern mining: current status and future directions (2007, methodology): “by Inokuchi et al. (1998), FSG by Kuramochi and Karypis (2001), and an edgedisjoint path-join algorithm by Vanetik et al.”
Frequent Subtree Mining – An Overview (2004, background): “[20] presented the AGM breadth-first algorithm for mining frequent unconnected graphs in a graph database.”
ANF: a fast and scalable tool for data mining in massive graphs (2002, methodology): “Systems for this include SUBDUE [5] and AGM [11].”
An efficient algorithm for discovering frequent subgraphs (2004, methodology): “However, a number of exact algorithms have been developed [10], [24], [25], [17], [45], [5] that guarantee to find all subgraphs that satisfy certain minimum support or other constraints.”
Efficient mining of frequent subgraphs in the presence of isomorphism (2003, methodology): “AGM finds all frequent induced subgraphs in a graph database.”