Calculates the local clustering coefficient for each node and returns the average clustering coefficient.
Inheritance Hierarchy
ClusteringCoefficient
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 number 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
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
See Also
The algorithm ignores self-loops and parallel edges. Group nodes are handled like common nodes.
Constructors
Parameters
options - Object
A map of options to pass to the method.
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 either sets the value directly or recursively sets properties to the instance of 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 either sets the value directly or recursively sets properties to the instance of 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: 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)))
}The edges provided here must be part of the graph which is passed to the run method.
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: 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)),
)The nodes provided here must be part of the graph which is passed to the run method.
Methods
Calculates the local clustering coefficient for each node and returns the average clustering coefficient.
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 number value between
0.0 and 1.0.Parameters
options - Object
A map of options to pass to the method.
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)))
}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.