class DualIso[TLabel] extends GraphMatcher[TLabel]
The DualIso class provides an implementation for Subgraph Isomorphism
that uses Dual Graph Simulation for pruning.
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val
CHECK: Int
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val
LIMIT: Double
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SELF_LOOPS: Boolean
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def
bijections(): Set[Array[Int]]
Apply the Dual Subgraph Isomorphism algorithm to find subgraphs of data graph 'g' that isomorphically match query graph 'q'.
Apply the Dual Subgraph Isomorphism 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' unction maps each query graph vertex 'u' to a data graph vertices 'v'.
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- DualIso → GraphMatcher
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clone(): AnyRef
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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
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- GraphMatcher
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final
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eq(arg0: AnyRef): Boolean
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equals(arg0: Any): Boolean
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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.
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- GraphMatcher
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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.
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finalize(): Unit
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val
gRange: Range
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def
mappings(): Array[Set[Int]]
Apply the Dual Subgraph Isomorphism pattern matching algorithm to find the mappings from the query graph 'q' to the data graph 'g'.
Apply the Dual Subgraph Isomorphism 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}'.
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- DualIso → GraphMatcher
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final
def
ne(arg0: AnyRef): Boolean
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final
def
notify(): Unit
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final
def
notifyAll(): Unit
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def
numMatches(): Int
Return the count of the number of matches.
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def
overlaps(set1: Set[Int], set2: Set[Int]): Boolean
Determine whether two sets overlap, i.e., have a non-empty intersection.
Determine whether two sets overlap, i.e., have a non-empty intersection.
- set1
the first set
- set2
the second set
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- GraphMatcher
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def
prune(phi: Array[Set[Int]]): Array[Set[Int]]
The 'prune' is not needed, pruning is delegated to incorporated graph simulation algorithm.
The 'prune' is not needed, pruning is delegated to incorporated graph simulation algorithm.
- phi
array of mappings from a query vertex u_q to { graph vertices v_g }
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- DualIso → GraphMatcher
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val
qRange: Range
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- GraphMatcher
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def
setLimit(_limit: Int): Unit
Set an upper bound on the number matches to allow before quitting.
Set an upper bound on the number matches to allow before quitting.
- _limit
the number of matches before quitting
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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
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- GraphMatcher
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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def
test(name: String, ans: Array[Set[Int]] = null): Unit
Test the Graph Pattern Matcher.
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toString(): String
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