Calculates the local clustering coefficient for each node and returns the average clustering coefficient.
Remarks
0.0 and 1.0.Definitions
- The local clustering coefficient of a node depends on the number of edges between its neighbors. A value of
1indicates that the node's neighbors form a clique (i.e., there is an edge between each pair of neighbors). - Average clustering coefficient is the average of all the local clustering coefficient values of a graph.
- Average weighted clustering coefficient is the average of all the local clustering coefficient values of a graph where each local value of a node is weighted by the maximum possible number of edges between its neighbors.
Examples
// prepare the algorithm
const algorithm = new ClusteringCoefficient({ directed: true })
// run the algorithm
const result = algorithm.run(graph)
// add the result as label
for (const node of graph.nodes) {
graph.addLabel(node, String(result.clusteringCoefficients.get(node)))
}Type Details
- yfiles module
- view-layout-bridge
- yfiles-umd modules
- view-layout-bridge
- Legacy UMD name
- yfiles.analysis.ClusteringCoefficient
See Also
Constructors
Creates a new instance of this class.
Parameters
A map of options to pass to the method.
- directed - boolean
A value indicating whether edge direction should be considered. This option sets the directed property on the created object.
- subgraphNodes - ItemCollection<INode>
The collection of nodes which define a subset of the graph for the algorithms to work on. This option sets the subgraphNodes property on the created object.
- subgraphEdges - ItemCollection<IEdge>
The collection of edges which define a subset of the graph for the algorithms to work on. This option sets the subgraphEdges property on the created object.
Properties
Gets or sets the collection of edges which define a subset of the graph for the algorithms to work on.
Remarks
If nothing is set, all edges of the graph will be processed.
If only the excludes are set all edges in the graph except those provided in the excludes are processed.
Note that edges which start or end at nodes which are not in the subgraphNodes are automatically not considered by the algorithm.
ItemCollection<T> instances may be shared among algorithm instances and will be (re-)evaluated upon (re-)execution of the algorithm.
Examples
// prepare the algorithm
const algorithm = new ClusteringCoefficient({
// Ignore edges without target arrow heads
subgraphEdges: {
excludes: (edge) =>
edge.style instanceof PolylineEdgeStyle &&
edge.style.targetArrow === IArrow.NONE
}
})
// run the algorithm
const result = algorithm.run(graph)
// add the result as label
for (const node of graph.nodes) {
graph.addLabel(node, String(result.clusteringCoefficients.get(node)))
}// prepare the algorithm
const algorithm = new ClusteringCoefficient({
// Ignore edges without target arrow heads
subgraphEdges: {
excludes: (edge: IEdge): boolean =>
edge.style instanceof PolylineEdgeStyle &&
edge.style.targetArrow === IArrow.NONE
}
})
// run the algorithm
const result = algorithm.run(graph)
// add the result as label
for (const node of graph.nodes) {
graph.addLabel(node, String(result.clusteringCoefficients.get(node)))
}Gets or sets the collection of nodes which define a subset of the graph for the algorithms to work on.
Remarks
If nothing is set, all nodes of the graph will be processed.
If only the excludes are set all nodes in the graph except those provided in the excludes are processed.
ItemCollection<T> instances may be shared among algorithm instances and will be (re-)evaluated upon (re-)execution of the algorithm.
Examples
// prepare the algorithm
const algorithm = new ClusteringCoefficient({
subgraphNodes: {
// only consider elliptical nodes in the graph
includes: (node) =>
node.style instanceof ShapeNodeStyle &&
node.style.shape === ShapeNodeShape.ELLIPSE,
// but ignore the first node, regardless of its shape
excludes: graph.nodes.first()
}
})
// run the algorithm
const result = algorithm.run(graph)
// add the result as label
result.clusteringCoefficients.forEach(
({ key: node, value: coefficient }) =>
graph.addLabel(node, String(coefficient))
)// prepare the algorithm
const algorithm = new ClusteringCoefficient({
subgraphNodes: {
// only consider elliptical nodes in the graph
includes: (node: INode): boolean =>
node.style instanceof ShapeNodeStyle &&
node.style.shape === ShapeNodeShape.ELLIPSE,
// but ignore the first node, regardless of its shape
excludes: graph.nodes.first()
}
})
// run the algorithm
const result = algorithm.run(graph)
// add the result as label
result.clusteringCoefficients.forEach(
({ key: node, value: coefficient }) =>
graph.addLabel(node, String(coefficient))
)Methods
Calculates the local clustering coefficient for each node and returns the average clustering coefficient.
Remarks
0.0 and 1.0.Parameters
A map of options to pass to the method.
- graph - IGraph
- the input graph
Returns
- ↪ClusteringCoefficientResult
- The coefficients for each node and the average coefficient.
Throws
- Exception({ name: 'InvalidOperationError' })
- If the algorithm can't create a valid result due to an invalid graph structure or wrongly configured properties.
Examples
// prepare the algorithm
const algorithm = new ClusteringCoefficient({ directed: true })
// run the algorithm
const result = algorithm.run(graph)
// add the result as label
for (const node of graph.nodes) {
graph.addLabel(node, String(result.clusteringCoefficients.get(node)))
}