C

GraphCentrality

Computes the graph centrality for the nodes of a graph.
Inheritance Hierarchy

Remarks

Graph centrality is defined as the reciprocal of the maximum of all shortest path distances from a node to all other nodes in the graph. Nodes with high graph centrality have short distances to all other nodes in the graph.

Other Centrality Measures

yFiles for HTML supports a number of other centrality measures:

  • ClosenessCentrality – emphasize nodes that have short paths to other nodes in a slightly different way
  • DegreeCentrality – emphasizes nodes with many edges
  • WeightCentrality – emphasizes nodes with highly-weighted edges
  • BetweennessCentrality – emphasizes nodes and edges that are part of many short paths
  • EigenvectorCentrality – computes the influence a node has on a network. The centrality value is higher if more nodes are connected to that node
  • PageRank – computes page rank values for all nodes based on their attached edges

Complexity

  • O(|V|² + |V| ⋅ |E|) for unweighted graphs
  • O(|V| ⋅ |E| + |V|² ⋅ log(|V|)) for weighted graphs

Examples

const result = new GraphCentrality({
  directed: true,
  // Use the geometric edge length as weight
  weights: (edge) =>
    edge.style.renderer
      .getPathGeometry(edge, edge.style)
      .getPath()!
      .getLength(),
}).run(graph)

// add node labels for centrality values
// and adjust node size according to centrality
result.normalizedNodeCentrality.forEach(({ key, value }) => {
  const node = key
  const centrality = value
  graph.addLabel(key, String(value))
  graph.setNodeLayout(
    node,
    new Rect(node.layout.center, new Size(centrality, centrality)),
  )
})

See Also

Developer's Guide

API

graphCentrality

Members

No filters for this type

Constructors

Parameters

Properties

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.
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 graph centrality on a subset of the graph
// configure the algorithm
const algorithm = new GraphCentrality({
  directed: true,
  // Use the geometric edge length as weight
  weights: (edge) =>
    edge.style.renderer
      .getPathGeometry(edge, edge.style)
      .getPath()!
      .getLength(),
  // 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 node labels for centrality values
// and adjust node size according to centrality
result.normalizedNodeCentrality.forEach((entry) => {
  const node = entry.key
  const centrality = entry.value
  graph.addLabel(node, `${centrality}`)
  graph.setNodeLayout(
    node,
    new Rect(node.layout.center, new Size(centrality, centrality)),
  )
})
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 graph centrality on a subset of the graph
// configure the algorithm
const algorithm = new GraphCentrality({
  directed: true,
  // Use the geometric edge length as weight
  weights: (edge) =>
    edge.style.renderer
      .getPathGeometry(edge, edge.style)
      .getPath()!
      .getLength(),
  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 node labels for centrality values
// and adjust node size according to centrality
result.normalizedNodeCentrality.forEach((entry) => {
  const node = entry.key
  const centrality = entry.value
  graph.addLabel(node, `${centrality}`)
  graph.setNodeLayout(
    node,
    new Rect(node.layout.center, new Size(centrality, centrality)),
  )
})
Gets or sets a mapping for edge weights.

Graph centrality computes shortest paths throughout the graph. Edge weights influence the computed length of those paths and thus the centrality measure. If no weights are provided, all edges have the same uniform weight of 1 and the number of edges is effectively the shortest path length.

Edge weights for graph centrality must be positive.

conversionfinal

Methods

Computes the graph centrality for the nodes of a graph.
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 to run the algorithm on.

Return Value

GraphCentralityResult
A GraphCentralityResult from which the calculated centrality values can be obtained.

Throws

Exception ({ name: 'InvalidOperationError' })
If the algorithm can't create a valid result due to an invalid graph structure or wrongly configured properties.

Complexity

  • O(|V|² + |V| ⋅ |E|) for unweighted graphs
  • O(|V| ⋅ |E| + |V|² ⋅ log(|V|)) for weighted graphs