C

ClusteringCoefficient

Show Usages
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

No filters for this type

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 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.

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 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.

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)))
}