Computes an eigenvector centrality for each node of a given undirected, unweighted graph.
Remarks
Eigenvector centrality is a measure of the influence a node has on a network: The more nodes point to a node the higher is that node's centrality.
The centrality values are scaled so that the largest centrality value is 1.0.
Other Centrality Measures
@PRODUCT@ supports a number of other centrality measures:
- GraphCentrality, ClosenessCentrality – emphasize nodes that have short paths to other nodes
- 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
- PageRank – computes page rank values for all nodes based on their attached edges
Examples
const result = new EigenvectorCentrality().run(graph)
// add node labels for centrality values
// and adjust node size according to centrality
result.nodeCentrality.forEach(({ key, value }) => {
const node = key
const centrality = value
graph.addLabel(node, String(centrality))
graph.setNodeLayout(
node,
new Rect(node.layout.center, new Size(centrality, centrality)),
)
})Type Details
- yFiles module
- view-layout-bridge
See Also
false isValid of the result. In such cases, we recommend to switch to the PageRank algorithm.Constructors
Parameters
A map of options to pass to the method.
- precision - number
- The precision used during the calculation of the power iteration method, i.e., the maximum possible difference to consider two values as equal. This option sets the precision 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
// configure the algorithm
const algorithm = new EigenvectorCentrality({
// 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 labels for centrality values
result.nodeCentrality.forEach(({ key, value }) =>
graph.addLabel(key, String(value)),
)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
// configure the algorithm
const algorithm = new EigenvectorCentrality({
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 labels for centrality values
result.nodeCentrality.forEach(({ key, value }) =>
graph.addLabel(key, String(value)),
)Methods
Computes an eigenvector centrality for each node of a given undirected, unweighted graph.
Remarks
Eigenvector centrality is a measure of the influence a node has on a network: The more nodes point to a node the higher is that node's centrality.
The centrality values are scaled so that the largest centrality value is 1.0.
Parameters
A map of options to pass to the method.
- graph - IGraph
- The input graph to run the algorithm on.
Returns
- ↪EigenvectorCentralityResult
- A EigenvectorCentralityResult 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.
Examples
const result = new EigenvectorCentrality().run(graph)
// add node labels for centrality values
// and adjust node size according to centrality
result.nodeCentrality.forEach(({ key, value }) => {
const node = key
const centrality = value
graph.addLabel(node, String(centrality))
graph.setNodeLayout(
node,
new Rect(node.layout.center, new Size(centrality, centrality)),
)
})