class GraphSimIso extends GraphMatcher
The GraphSimIso object provides an implementation for Subgraph Isomorphism
that uses an adjacency set version of Ullmann's Algorithm.
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def
!=(arg0: Any): Boolean
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##(): Int
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val
CHECK: Int
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val
LIMIT: Double
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val
SELF_LOOPS: Boolean
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def
bijections(): Set[Array[Int]]
Apply the Ullmann's Subgraph Isomorphism algorithm to find subgraphs of data graph 'g' that isomorphically match query graph 'q'.
Apply the Ullmann's 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' function maps each query graph vertex 'u' to a data graph vertices 'v'.
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- GraphSimIso → GraphMatcher
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def
clone(): AnyRef
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def
disjoint(set1: Set[Int], set2: Set[Int]): Boolean
Determine whether two sets are disjoint, i.e., have an empty intersection.
Determine whether two sets are disjoint, i.e., have an empty intersection.
- set1
the first set
- set2
the second set
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- GraphMatcher
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final
def
eq(arg0: AnyRef): Boolean
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def
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
finalize(): Unit
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final
def
getClass(): Class[_]
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def
getMatches(): Int
Get the count of the number of matches.
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def
hashCode(): Int
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final
def
isInstanceOf[T0]: Boolean
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def
makeOrder(): Array[Int]
Establish a vertex order from largest to smallest adjacency set ('ch') size.
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def
mappings(): Array[Set[Int]]
Apply a graph pattern matching algorithm to find the mappings from the query graph 'q' to the data graph 'g'.
Apply a graph 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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final
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ne(arg0: AnyRef): Boolean
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notify(): Unit
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def
notifyAll(): Unit
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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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- GraphSimIso → GraphMatcher
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val
qRange: Range
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- GraphMatcher
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def
showMappings(phi: Array[Set[Int]]): Unit
Show the mappings between a query graph vertex u and a set of data graph vertices {v}.
Show the mappings between a query graph vertex u and a set 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.
Test the graph pattern matcher.
- name
the name of graph pattern matcher
- ans
the correct answer
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- GraphMatcher
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def
toString(): String
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def
wait(): Unit
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def
wait(arg0: Long, arg1: Int): Unit
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wait(arg0: Long): Unit
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