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Rely on layout and clustering algorithms from networkanalysis-ts pack…
…age (#22) * Implement new network data structure * Implement new Leiden algorithm * Implement new VOS algorithm * Remove old algorithms and data structures * Add networkanalysis-ts package dependency
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,59 +1,56 @@ | ||
| import Random from 'java-random'; | ||
| import ClusteringTechnique from './ClusteringTechnique'; | ||
| import { Clustering, LeidenAlgorithm } from 'networkanalysis-ts'; | ||
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| export const DEFAULT_RESOLUTION = 1.0; | ||
| export const DEFAULT_MIN_CLUSTER_SIZE = 1; | ||
| export const DEFAULT_MERGE_SMALL_CLUSTERS = true; | ||
| export const DEFAULT_N_ITERATIONS = 10; | ||
| export const DEFAULT_RANDOMNESS = 0.01; | ||
| export const DEFAULT_N_RANDOM_STARTS = 10; | ||
| export const DEFAULT_FIXED_SEED = 0; | ||
| export const DEFAULT_USE_RANDOM_SEED = false; | ||
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| export class ClusteringCreator { | ||
| constructor() { | ||
| this.bestClustering = undefined; | ||
| this.maxQualityFunction = -Infinity; | ||
| } | ||
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| init(network, parameters, useLinLogModularityNormalization) { | ||
| this.network = network; | ||
| this.resolution = parameters.resolution; | ||
| this.minClusterSize = parameters.minClusterSize; | ||
| this.mergeSmallClusters = parameters.mergeSmallClusters; | ||
| this.nIterations = parameters.nIterations; | ||
| this.randomness = parameters.randomness; | ||
| this.nRandomStarts = parameters.nRandomStarts; | ||
| this.fixedSeed = parameters.fixedSeed; | ||
| this.useRandomSeed = parameters.useRandomSeed; | ||
| this.useLinLogModularityNormalization = useLinLogModularityNormalization; | ||
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| this.random = this.useRandomSeed ? new Random() : new Random(this.fixedSeed); | ||
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| let resolution2 = parameters.resolution; | ||
| if (this.useLinLogModularityNormalization) { | ||
| resolution2 /= (2 * this.network.getTotalEdgeWeight()); | ||
| } | ||
| this.clusteringAlgorithm = new LeidenAlgorithm(); | ||
| this.clusteringAlgorithm.initializeBasedOnResolutionAndNIterationsAndRandomnessAndRandom(resolution2, this.nIterations, this.randomness, this.random); | ||
| this.bestClustering = undefined; | ||
| this.maxQualityFunction = -Infinity; | ||
| this.maxQuality = -Infinity; | ||
| this.randomStart = 0; | ||
| } | ||
|
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||
| performRandomStart() { | ||
| let { resolution } = this; | ||
| if (this.useLinLogModularityNormalization) { | ||
| resolution /= (2 * this.network.getTotalEdgeWeight()); | ||
| } | ||
| const clusteringTechnique = new ClusteringTechnique({ network: this.network, resolution }); | ||
| clusteringTechnique.runIteratedSmartLocalMovingAlgorithm(this.nIterations, this.random); | ||
| const qualityFunction = clusteringTechnique.calcQualityFunction(); | ||
| if ((this.bestClustering === undefined) || (qualityFunction > this.maxQualityFunction)) { | ||
| this.bestClustering = clusteringTechnique.getClustering(); | ||
| this.maxQualityFunction = qualityFunction; | ||
| const clustering = new Clustering({ nNodes: this.network.getNNodes() }); | ||
| this.clusteringAlgorithm.improveClustering(this.network, clustering); | ||
| const quality = this.clusteringAlgorithm.calcQuality(this.network, clustering); | ||
| if ((this.bestClustering === undefined) || (quality > this.maxQuality)) { | ||
| this.bestClustering = clustering; | ||
| this.maxQuality = quality; | ||
| } | ||
| this.randomStart += 1; | ||
| } | ||
|
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| performPostProcessing() { | ||
| const { resolution, mergeSmallClusters, minClusterSize } = this; | ||
| const clusteringTechnique = new ClusteringTechnique({ network: this.network, clustering: this.bestClustering, resolution }); | ||
| if (mergeSmallClusters) { | ||
| clusteringTechnique.removeSmallClustersBasedOnNNodes(minClusterSize); | ||
| if (this.mergeSmallClusters) { | ||
| this.clusteringAlgorithm.removeSmallClustersBasedOnNNodes(this.network, this.bestClustering, this.minClusterSize); | ||
| } | ||
|
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||
| this.bestClustering = clusteringTechnique.getClustering(); | ||
| this.bestClustering.orderClustersByNNodes(); | ||
| } | ||
| } |
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