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Discovering Frequent Graph Patterns Using Disjoint Paths

https://doi.org/10.1109/tkde.2006.173

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What this work says

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Isomorph-Free Exhaustive Generation

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Index Structures for Path Expressions

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Finding Frequent Patterns in a Large Sparse Graph*

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Support measures for graph data*

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Subsea: an efficient heuristic algorithm for subgraph isomorphism

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Complete Mining of Frequent Patterns from Graphs: Mining Graph Data

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inferredcites →https://doi.org/10.1109/icdm.2001.989534openalex-snapshot

Frequent subgraph discovery

inferredcites →https://doi.org/10.1109/icdm.2002.1183988openalex-snapshot

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gSpan: graph-based substructure pattern mining

inferredcites →https://doi.org/10.1109/icdm.2003.1250974openalex-snapshot

Efficient mining of frequent subgraphs in the presence of isomorphism

inferredcites →https://doi.org/10.1109/icsmc.2004.1401252openalex-snapshot

Frequent graph mining and its application to molecular databases

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Finding patterns in three-dimensional graphs: algorithms and applications to scientific data mining

inferredcites →https://doi.org/10.1109/tkde.2004.33openalex-snapshot

An efficient algorithm for discovering frequent subgraphs

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Efficiently computing frequent tree-like topology patterns in a Web environment

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Finding Frequent Patterns in a Large Sparse Graph

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Diagonally Subgraphs Pattern Mining

inferredcites →https://doi.org/10.1145/1014052.1014088openalex-snapshot

Scalable mining of large disk-based graph databases

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Storing semistructured data with STORED

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The design and performance evaluation of alternative XML storage strategies

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Algorithmics and applications of tree and graph searching

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APEX

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XQuery

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CloseGraph

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State of the art of graph-based data mining

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XQuery: An XML query language

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A Extensible Markup Language XML

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Substructure Discovery Using Minimum Description Length and Background Knowledge

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Substructure Discovery Using Minimum Description Length and Background Knowledge

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Frequent Subtree Mining - An Overview

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An efficient algorithm of frequent connected subgraph extraction

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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TED: Towards Discovering Top-k Edge-Diversified Patterns in a Graph Database

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