About projectedFeatureDimension in graphSage model

[lingvisa]

github.com

zach-blumenfeld/demo-news-recommendation/blob/main/graphsage-example.cypher

//1 projection
CALL gds.graph.create(
  'mind-gs-projection',
  {
    User: {},
    Category: {},
    Subcategory: {},
    WikiEntity: {properties: {wikiEncoding: {defaultValue: [i IN range(1,100) | 0.0]}}},
    News: {}
  },
  {
    CLICKED:{orientation:'UNDIRECTED'},
    HISTORICALLY_CLICKED:{orientation: 'UNDIRECTED'},
    BELONGS_TO_SUBCATEGORY:{orientation: 'UNDIRECTED'},
    SUBCATEGORY_OF:{orientation: 'UNDIRECTED'},
    ABSTRACT_ABOUT:{orientation: 'UNDIRECTED'},
    SUBCATEGORY_OF:{orientation: 'UNDIRECTED'}
  }
) YIELD nodeCount, relationshipCount, createMillis

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In today's seminar, the MS News graphSage model configuration is below:

CALL gds.beta.graphSage.train(
  'mind-gs-projection',
  {
    modelName: 'graphSage',
    featureProperties: ['wikiEncoding'],
    aggregator: 'mean',
    activationFunction: 'sigmoid',
    projectedFeatureDimension: 10,
    sampleSizes: [25, 10],
    tolerance: 0.001,
    searchDepth: 5,
    embeddingDimension: 128
  }
)

The data preprocessing creates entity embedding of 100 dimension. And according the documentation:
The projectedFeatureDimensionequals the maximum length of the feature-array, i.e.,ageandcostboth are scalar features plus the list featureheightAndWeightwhich has a length of two. For each node its unique labels properties is projected using a label specific projection to vector space of dimensionprojectedFeatureDimension .

So the projectedFeatureDimension should be at least 100 in the cypher command above. But why is it only 10? Please clarify the proper choice of this parameter.

[lingvisa]

Please take a look at this question.