C

ClusteringCoefficient

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Calculates the local clustering coefficient for each node and returns the average clustering coefficient.
Inheritance Hierarchy

Remarks

The clustering coefficient measures the degree to which the nodes of a network tend to cluster together, see https://en.wikipedia.org/wiki/Clustering_coefficient. More precisely, for a node n, the local clustering coefficient is the actual number of edges between the neighbors of n divided by the maximum possible number of such edges. Hence, it is always a value between 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 1 indicates 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

Calculating the clustering coefficient.
// 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)))
}

See Also

API

clusteringCoefficient

Members

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Constructors

ClusteringCoefficient

()

Parameters

Properties

directed

: boolean
Gets or sets a value indicating whether edge direction should be considered.
Default is true.
final

Property Value

true if the graph should be considered as directed, false otherwise.

subgraphEdges

: ItemCollection<IEdge>
Gets or sets the collection of edges which define a subset of the graph for the algorithms to work on.

If nothing is set, all edges of the graph will be processed.

If only the ItemCollection<TItem>.excludes are set, all edges in the graph except those provided in the ItemCollection<TItem>.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.

The edges provided here must be part of the graph which is passed to the run method.
conversionfinal

Examples

Calculating the clustering coefficient on a subset of the graph
// 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 instanceof Arrow &&
      edge.style.targetArrow.type === ArrowType.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)))
}

subgraphNodes

: ItemCollection<INode>
Gets or sets the collection of nodes which define a subset of the graph for the algorithms to work on.

If nothing is set, all nodes of the graph will be processed.

If only the ItemCollection<TItem>.excludes are set, all nodes in the graph except those provided in the ItemCollection<TItem>.excludes are processed.

ItemCollection<T> instances may be shared among algorithm instances and will be (re-)evaluated upon (re-)execution of the algorithm.

The nodes provided here must be part of the graph which is passed to the run method.
conversionfinal

Examples

Calculating the clustering coefficient on a subset of the graph
// 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

run

(graph: IGraph): ClusteringCoefficientResult
Calculates the local clustering coefficient for each node and returns the average clustering coefficient.
The clustering coefficient measures the degree to which the nodes of a network tend to cluster together, see https://en.wikipedia.org/wiki/Clustering_coefficient. More precisely, for a node n, the local clustering coefficient is the actual number of edges between the neighbors of n divided by the maximum possible number of such edges. Hence, it is always a value between 0.0 and 1.0.
The algorithm ignores self-loops and parallel edges. Group nodes are handled like common nodes.
The result obtained from this algorithm is a snapshot which is no longer valid once the graph has changed, e.g. by adding or removing nodes or edges.
final

Parameters

graph: IGraph
the input graph

Return Value

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

Calculating the clustering coefficient.
// 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)))
}