Mining Preserving Structures in a Graph Sequence View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2015

AUTHORS

Takeaki Uno , Yushi Uno

ABSTRACT

In the recent research of data mining, frequent structures in a sequence of graphs have been studied intensively, and one of the main concern is changing structures along a sequence of graphs that can capture dynamic properties of data. On the contrary, we newly focus on “preserving structures” in a graph sequence that satisfy a given property for a certain period, and mining such structures is studied. We bring up two structures of practical importance, a connected vertex subset and a clique that exist for a certain period. We consider the problem of enumerating these structures and present polynomial delay algorithms for the problems. Their running time may depend on the size of the representation, however, if each edge has at most one time interval in the representation, the running time is \(O(|V| |E|^3)\) for connected vertex subsets and \(O(\min \{\Delta ^5, |E|^2 \Delta \})\) for cliques, where the input graph is \(G=(V, E)\) with maximum degree \(\Delta \). To the best of our knowledge, this is the first systematic approach to the treatment of this notion, namely, preserving structures. More... »

PAGES

3-15

References to SciGraph publications

  • 2009. Mining Graph Evolution Rules in MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES
  • 2007. Time and Space Efficient Discovery of Maximal Geometric Graphs in DISCOVERY SCIENCE
  • 2003-03. Complete Mining of Frequent Patterns from Graphs: Mining Graph Data in MACHINE LEARNING
  • 2013. Trajectory Grouping Structure in ALGORITHMS AND DATA STRUCTURES
  • 2004. New Algorithms for Enumerating All Maximal Cliques in ALGORITHM THEORY - SWAT 2004
  • 2005. On Discovering Moving Clusters in Spatio-temporal Data in ADVANCES IN SPATIAL AND TEMPORAL DATABASES
  • Book

    TITLE

    Computing and Combinatorics

    ISBN

    978-3-319-21397-2
    978-3-319-21398-9

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-21398-9_1

    DOI

    http://dx.doi.org/10.1007/978-3-319-21398-9_1

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1039536391


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