layout_network
Compute a force-directed graph layout with a fast forceAtlas2 implementation.
Usage
The following example shows how the step can be used in a recipe.
For a weighted layout provide both input columns, e.g.
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").
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).
Attracts nodes to the center. Prevents islands from drifting away.
The amount of repulsion. Greater values lead to a larger and more sparse graph.
Algorithmic “resolution”. Greater values lead to faster execution at the expense of less precise calculations.
Values must be in the following range:
Prefer authorities over hubs. Prefer “authorities” (nodes with a high indegree) over hubs (nodes with a high outdegree). Authorities will have more central and hubs more peripheral positions. (default=false).
Usually produces tighter clusters. Enabling it may also require adjusting the scalingRatio.
Try to avoid overlap between nodes.
The more the better, though it will take longer.
How much space to (try and) give each node in the final layout.
Values must be in the following range:
Links with weights below this value will be ignored.
Values must be in the following range:
Normalize weights to the range [0, 1].
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