Detects the communities in the specified input graph by applying the Louvain modularity method.
Remarks
Louvain Modularity algorithm iteratively tries to construct communities by moving nodes from their current community to another until the modularity is locally optimized.
The implementation is based on the description in: "Fast unfolding of communities in large networks" by V.D. Blondel, J.L. Guillaume, R. Lambiotte and E. Lefebvre, in Journal of Statistical Mechanics: Theory and Experiment 2008 (10), P10008 (12pp).
The algorithm starts by assigning each node to its own community. Then, iteratively tries to construct communities by moving nodes from their current community to another until the modularity is locally optimized. At the next step, the small communities found are merged to a single node and the algorithm starts from the beginning until the modularity of the graph cannot be further improved.
If no weights are given, the algorithm will assume that all edges have edge weights equal to 1.
Other Clustering Algorithms
@PRODUCT@ supports a number of other clustering algorithms:
- EdgeBetweennessClustering – partitions the graph into clusters based on edge-betweenness centrality.
- KMeansClustering – partitions the graph into clusters based on the distance between nodes and the cluster midpoints.
- HierarchicalClustering – partitions the graph into clusters by merging smaller clusters based on their distance.
- BiconnectedComponentClustering – partitions the graph into clusters based on its biconnected components.
- LabelPropagationClustering – partitions the graph into clusters by applying a label propagation algorithm.
Examples
// prepare the louvain modularity algorithm
const algorithm = new LouvainModularityClustering()
// run the algorithm
const result = algorithm.run(graph)
// highlight the nodes of the clusters with different styles
for (const node of graph.nodes) {
const componentId = result.nodeClusterIds.get(node)!
graph.setStyle(node, clusterStyles.get(componentId)!)
}Type Details
- yFiles module
- view-layout-bridge
See Also
Constructors
Parameters
A map of options to pass to the method.
- weights - ItemMapping<IEdge,number>
- A mapping for edge weights. This option either sets the value directly or recursively sets properties to the instance of the weights 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 louvain modularity algorithm
const algorithm = new LouvainModularityClustering({
// 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)
// highlight the nodes of the clusters with different styles
for (const node of graph.nodes) {
const componentId = result.nodeClusterIds.get(node)!
graph.setStyle(node, clusterStyles.get(componentId)!)
}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 louvain modularity algorithm
const algorithm = new LouvainModularityClustering({
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)
// highlight the nodes of the clusters with different styles
for (const node of graph.nodes) {
const componentId = result.nodeClusterIds.get(node)!
graph.setStyle(node, clusterStyles.get(componentId)!)
}Gets or sets a mapping for edge weights.
Remarks
1.Methods
Detects the communities in the specified input graph by applying the Louvain modularity method.
Remarks
The algorithm starts by assigning each node to its own community. Then, iteratively tries to construct communities by moving nodes from their current community to another until the modularity is locally optimized. At the next step, the small communities found are merged to a single node and the algorithm starts from the beginning until the modularity of the graph cannot be further improved.
The community index of a node corresponds to the index of the associated community. If there are nodes that are not associated with a community, their index is -1.
If no weights are given, the algorithm will assume that all edges have edge weights equal to 1.
Parameters
A map of options to pass to the method.
- graph - IGraph
- The graph to partition.
Returns
- ↪LouvainModularityClusteringResult
- The calculated clusters.
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 louvain modularity algorithm
const algorithm = new LouvainModularityClustering()
// run the algorithm
const result = algorithm.run(graph)
// highlight the nodes of the clusters with different styles
for (const node of graph.nodes) {
const componentId = result.nodeClusterIds.get(node)!
graph.setStyle(node, clusterStyles.get(componentId)!)
}