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For each pair of nodes connected by a link that indicates a similarity greater than a specified threshold, keeps only one of the two nodes and rewires the deleted node’s incoming and outgoing links to point to the “surviving” node.

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Usage

The following example shows how the step can be used in a recipe.

Examples

To de-duplicate pairs of nodes with a link weight (similarity) greater than 0.9
filter_duplicate_nodes(network, {
  "duplicate_threshold": 0.9
}) -> (network_filtered)

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Inputs & Outputs

The following are the inputs expected by the step and the outputs it produces. These are generally columns (ds.first_name), datasets (ds or ds[["first_name", "last_name"]]) or models (referenced by name e.g. "churn-clf").
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network
dataset
required
dataset containing the nodes (rows) to de-duplicate and the links between nodes of the input dataset.
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network_flt
dataset
required
A new dataset containing the same columns as the input data, but without duplicate rows and having connections rewired such that none points to a deleted node.

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Configuration

The following parameters can be used to configure the behaviour of the step by including them in a json object as the last “input” to the step, i.e. step(..., {"param": "value", ...}) -> (output).

Parameters

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duplicate_threshold
[number, null]
required
Similarity threshold for candidate nodes to be eliminated. Any node linked to another node with a weight (usually similarity) greater than this value will be eliminated. Default (null) corresponds to positive infinity (no de-duplication).