Efficient mining of frequent subgraphs in the presence of isomorphism

By Jun Huan, Wei Wang, Jan F. Prins. 2003

730 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Signal Processing

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Citations in the corpus

Text Categorization as a Graph Classification Problem (2015, methodology): “Some popular graph pattern mining algorithms are gSpan (Yan and Han, 2002), FFSM (Huan et al., 2003) and Gaston (Nijssen and Kok, 2004).”
Bridging semantics and syntax with graph algorithms - state-of-the-art of extracting biomedical relations (2017, background): “Shared resources for biomedical relation extraction Utility category Data sources Terminology/Ontology/Knowledge Base GO [161], UMLS [58], MeSH [162], Wordnet [118], Verbnet [163], Biothesaurus [122], PDB [101], Uniprot [121], SIDER [164], DrugBank [165], HIVDB [166], RegaDB [167], Entrez Gene [153]”
Finding Frequent Patterns in a Large Sparse Graph* (2004, methodology): “In recent years, a number of efficient and scalable algorithms have been developed to find patterns in the graph-transaction setting (Borgelt and Berthold, 2002; Yan and Han, 2002; Inokuchi et al., 2003; Hong et al., 2003; Yan and Han, 2003; Huan et al., 2003; Kuramochi and Karypis, 2004a).”
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”
Text Classification using Graph Mining-based Feature Extraction (2010, background): “Frequent subgraph (and sub-tree) mining, using various app ro ches, has been extensively studied [9, 10, 22, 8, 2].”
A survey of frequent subgraph mining algorithms (2012, background): “Examples of keeping embeddings are FFSM and GASTON, and instances of using subgraph isomorphism are FSG and gSpan.”
A quickstart in frequent structure mining can make a difference (2004, methodology): “Diﬀer-ent codes have been proposed, among which DFS codes (in gSpan [14]), adjacency matrices (in FFSM [5]) and tree codes with backtrack symbols (in FreeTreeMiner [2] and TreeMiner [15]).”
Multiple Structure-View Learning for Graph Classification (2018, background): “, FFSM [18] and 182 PSFS [19]) have also been proposed to find frequent subgraph 183 features for further learning.”
Frequent substructure-based approaches for classifying chemical compounds (2003, methodology): “These are the AGM algorithm developed by Inokuchi et al [40], the FSG algorithm developed by members of our group [47], [50], the chemical sub-structure discovery algorithm developed by Borgelt and Berthold [19], the gSpan algorithm developed by Yan and Han [82], the FFSM by Huan, Wan and Prins [39]”
Efficiently mining frequent trees in a forest: algorithms and applications (2005, methodology): “Another method uses a candidate generation approach based on Canonical Adjacency Matrices [17].”
Frequent pattern mining: current status and future directions (2007, methodology): “Pattern-growth-based graph pattern mining algorithms include gSpan by Yan and Han (2002), MoFa by Borgelt and Berthold (2002), FFSM by Huan et al. (2003), SPIN by Huan et al. (2004), and Gaston by Nijssen and Kok (2004).”
Frequent sub-structure-based approaches for classifying chemical compounds (2005, methodology): “These are the AGM algorithm developed by Inokuchi et al. [40], the FSG algorithm developed by members of our group [47], [50], the chemical substructure discovery algorithm developed by Borgelt and Berthold [19], the gSpan algorithm developed by Yan and Han [82], the FFSM by Huan et al. [ 39 ], and,”
Frequent Subtree Mining – An Overview (2004, neutral): “The frequent connected subgraph problem was also tackled by Yan et al. [45, 46] in the pure depth-rst algorithm gSpan and by Huan et al. [ 19 ] in the combined depthrst/breadth-rst algorithm FFSM.”
Near-optimal Supervised Feature Selection among Frequent Subgraphs (2009, methodology): “To avoid this overhead, non-Apriori-based algorithms such as gSpan [34], MoFa [2], FFSM [14], and Gaston [24] adopt the patterngrowth methodology, which attempts to extend graphs from a single subgraph directly.”
SPIN: mining maximal frequent subgraphs from graph databases (2004, methodology): “Practically we do observe a sharp increase of total number of subgraphs when we mine even moderate sized graph databases [10].”