class MTightSimCAR[TLabel] extends GraphMatcher[TLabel]
The 'MTightSimCAR' class provides an implementation for tight simulation
graph pattern matching. This version uses DualSim.
- See also
hipore.com/ijbd/2014/IJBD%20Vol%201%20No%201%202014.pdf
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val
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val
LIMIT: Double
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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
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def
calculateBallDiameterMetrics(balls: Map[Int, Ball[TLabel]]): Statistic
Calculate statistics (e.g., min, max, avg diameter and standard deviation) on the balls left after postprocessing.
Calculate statistics (e.g., min, max, avg diameter and standard deviation) on the balls left after postprocessing.
- balls
mappings from a center vertex to the Ball B(Graph, Center, Radius)
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def
calculateTotalEdges(g: MGraph[TLabel], balls: Map[Int, Ball[TLabel]], matchCenters: Set[Int]): Int
Count distinct edges left after post processing.
Count distinct edges left after post processing.
- g
the data graph G(V, E, l)
- balls
mappings from a center vertex to the Ball B(Graph, Center, Radius)
- matchCenters
set of all vertices which are considered as center
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def
calculateTotalVertices(): Int
Count distinct vertices left after post processing.
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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
- Definition Classes
- GraphMatcher
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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)
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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.
- Definition Classes
- 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.
- Definition Classes
- GraphMatcher
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def
filterGraph(phi: Array[Set[Int]]): Graph[TLabel]
Prune the data graph by consider only those vertices and edges which are part of feasible matches after performing initial dual simulation.
Prune 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 }
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def
finalize(): Unit
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val
gRange: Range
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hashCode(): Int
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final
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isInstanceOf[T0]: Boolean
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def
mappings(): Array[Set[Int]]
Apply the Tight Graph Simulation pattern matching algorithm to find the mappings from the query graph 'q' to the data graph 'g'.
Apply the Tight 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
- MTightSimCAR → GraphMatcher
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def
mappings2(): Map[Int, Array[Set[Int]]]
Return mapping results per ball.
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final
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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
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
- Definition Classes
- GraphMatcher
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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
- MTightSimCAR → GraphMatcher
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val
qRange: Range
- Attributes
- protected
- Definition Classes
- GraphMatcher
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def
refine(phi: Array[Set[Int]]): Map[Int, Array[Set[Int]]]
Refine 'phi' using tight simulation to find mappings with balls.
Refine 'phi' using tight simulation to find mappings with balls.
- phi
the initial mapping after applying Dual to the whole graph
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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.
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.
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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
- Definition Classes
- 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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def
toString(): String
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wait(): Unit
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