Packages

class MStrictSim[TLabel] extends GraphMatcher[TLabel]

The 'MStrictSim' class provides an implementation for strict simulation graph pattern matching. This version uses DualSim.

See also

hipore.com/ijbd/2014/IJBD%20Vol%201%20No%201%202014.pdf

Linear Supertypes
GraphMatcher[TLabel], AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. MStrictSim
  2. GraphMatcher
  3. AnyRef
  4. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Instance Constructors

  1. new MStrictSim(g: MGraph[TLabel], q: MGraph[TLabel], duals: GraphMatcher[TLabel])(implicit arg0: ClassTag[TLabel])

    g

    the data graph G(V, E, l)

    q

    the query graph Q(U, D, k)

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. val CHECK: Int
    Attributes
    protected
    Definition Classes
    GraphMatcher
  5. val LIMIT: Double
    Attributes
    protected
    Definition Classes
    GraphMatcher
  6. val SELF_LOOPS: Boolean
    Attributes
    protected
    Definition Classes
    GraphMatcher
  7. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  8. def bijections(): Set[Array[Int]]

    Apply a graph pattern matching algorithm to find subgraphs of data graph 'g' that isomorphically match query graph 'q'.

    Apply a graph pattern matching algorithm to find subgraphs of data graph 'g' that isomorphically match query graph 'q'. These are represented by a set of single-valued bijections {'psi'} where each 'psi' function maps each query graph vertex 'u' to a data graph vertices 'v'.

    Definition Classes
    GraphMatcher
  9. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  10. def countMappings(phi: Array[Set[Int]]): Pair

    Count the number of mappings between query graph vertices 'u_i' and their sets of data graph vertices {v}, giving the number of distinct vertices and edges.

    Count the number of mappings between query graph vertices 'u_i' and their sets of data graph vertices {v}, giving the number of distinct vertices and edges.

    phi

    the set-valued mapping function

    Definition Classes
    GraphMatcher
  11. def disjoint(set1: Set[Int], set2: Set[Int]): Boolean

    Determine whether two sets are disjoints, i.e., have an empty intersection.

    Determine whether two sets are disjoints, i.e., have an empty intersection.

    set1

    the first set

    set2

    the second set

    Definition Classes
    GraphMatcher
  12. def dualFilter(phi: Array[Set[Int]], ball: Ball[TLabel]): Array[Set[Int]]

    Perform dual simulation onto the ball.

    Perform dual simulation onto the ball.

    phi

    mappings from a query vertex u_q to { graph vertices v_g }

    ball

    the Ball B(Graph, Center, Radius)

  13. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  14. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  15. def feasibleMates(): Array[Set[Int]]

    Create an initial array of feasible mappings 'phi' from each query vertex 'u' to the corresponding set of data graph vertices '{v}' whose label matches 'u's.

    Create an initial array of feasible mappings 'phi' from each query vertex 'u' to the corresponding set of data graph vertices '{v}' whose label matches 'u's.

    Definition Classes
    GraphMatcher
  16. def feasibleMatesW(): Array[Set[Int]]

    Create an initial array of feasible mappings 'phi' from each query vertex 'u' to the corresponding set of data graph vertices '{v}' whose label matches 'u's.

    Create an initial array of feasible mappings 'phi' from each query vertex 'u' to the corresponding set of data graph vertices '{v}' whose label matches 'u's. This version handles query graph labels that have wildcards.

    Definition Classes
    GraphMatcher
  17. def filterGraph(phi: Array[Set[Int]]): Graph[TLabel]

    Filter the data graph by consider only those vertices and edges which are part of feasible matches after performing initial dual simulation.

    Filter the data graph by consider only those vertices and edges which are part of feasible matches after performing initial dual simulation.

    phi

    mappings from a query vertex u_q to { graph vertices v_g }

    Definition Classes
    GraphMatcher
  18. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  19. val gRange: Range
    Attributes
    protected
    Definition Classes
    GraphMatcher
  20. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  21. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  22. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  23. def mappings(): Array[Set[Int]]

    Apply the Strict Graph Simulation pattern matching algorithm to find the mappings from the query graph 'q' to the data graph 'g'.

    Apply the Strict Graph Simulation pattern matching algorithm to find the mappings from the query graph 'q' to the data graph 'g'. These are represented by a multi-valued function 'phi' that maps each query graph vertex 'u' to a set of data graph vertices '{v}'.

    Definition Classes
    MStrictSim → GraphMatcher
  24. def mappings2(): Map[Int, Array[Set[Int]]]

    Return mapping results per ball.

  25. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  26. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  27. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  28. def prune(phi: Array[Set[Int]]): Array[Set[Int]]

    The 'prune' is not needed, pruning is delegated to incorporated dual graph simulation algorithm.

    The 'prune' is not needed, pruning is delegated to incorporated dual graph simulation algorithm.

    phi

    array of mappings from a query vertex u_q to { graph vertices v_g }

    Definition Classes
    MStrictSim → GraphMatcher
  29. val qRange: Range
    Attributes
    protected
    Definition Classes
    GraphMatcher
  30. def refine(phi: Array[Set[Int]]): Map[Int, Array[Set[Int]]]

    Refine 'phi' using strict simulation to find mappings within balls.

    Refine 'phi' using strict simulation to find mappings within balls.

    phi

    the initial mapping after applying Dual to the whole graph

  31. def selectivityCriteria(qmet: GraphMetrics[TLabel]): Int

    Return the vertex from an array of central vertices, those which have highest 'ch' set size and lowest frequency of label in the query graph, i.e., highest ratio.

  32. def showMappings(phi: Array[Set[Int]]): Unit

    Show all mappings between query graph vertices 'u_i' and their sets of data graph vertices {v}.

    Show all mappings between query graph vertices 'u_i' and their sets of data graph vertices {v}.

    phi

    the set-valued mapping function

    Definition Classes
    GraphMatcher
  33. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  34. def test(name: String, ans: Array[Set[Int]] = null): Array[Set[Int]]

    Test the Graph Pattern Matcher.

    Test the Graph Pattern Matcher.

    ans

    the correct answer

    Definition Classes
    GraphMatcher
  35. def toString(): String
    Definition Classes
    AnyRef → Any
  36. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  37. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  38. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )

Inherited from GraphMatcher[TLabel]

Inherited from AnyRef

Inherited from Any

Ungrouped