This module implements Chalmers' 1996 Neighbour and Sampling algorithm for drawing the force-directed layouts. It is a linear time algorithm that uses stochastic sampling to find the best neighbours for high-dimensional data and creates the layout in 2 dimensions.

Neighbour and Sampling algorithm is useful for producing visualizations that show relationships between the data. For instance:

Authors

Remigijus Bartasius and Matthew Chalmers

Reference

Chalmers, Matthew. "A linear iteration time layout algorithm for visualising high-dimensional data." Proceedings of the 7th conference on Visualization'96. IEEE Computer Society Press, 1996.

Installing

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API Reference

NeighbourSampling

The Neighbour and Sampling algorithm tries to group the nodes based on the distance between them. If the nodes have a low distance, then the force attracts them to each other. If the nodes have a high distance, then the repulsive force pushes them further apart from each other.

In order for it to work properly, a distance function should be specified.

# d3.forceNeighbourSampling() <>

Initializes the Neighbour and Sampling algorithm with default parameters.

# neighbourSampling.id([id]) <>

If id is specified, sets the node id accessor to the specified function and returns this force. If id is not specified, returns the current node id accessor, which defaults to the numeric node.index:

function id(d) {
  return d.index;
}

The id accessor is invoked for each node whenever the force is initialized, as when the nodes change, being passed the node and its zero-based index.

# neighbourSampling.distance([distance]) <>

If distance is specified, sets the distance accessor to the specified number or function, re-evaluates the distance accessor for each link, and returns this force. If distance is not specified, returns the current distance accessor, which defaults to:

function distance() {
  return 300;
}

# neighbourSampling.neighbourSize([neighbourSize]) <>

If neighbourSize is specified, sets the neighbour set size to the specified number and returns this force. If neighbourSize is not specified, returns the current value, which defaults to 6.

# neighbourSampling.sampleSize([sampleSize]) <>

If sampleSize is specified, sets the sample set size to the specified number and returns this force. If sampleSize is not specified, returns the current value, which defaults to 3.

# neighbourSampling.stress() <>

Returns the stress of the layout.

# neighbourSampling.velocity() <>

Returns the average velocity of the iteration.