Wrapper function for the metapath2vec parameters

Parameters for metapath2vec(). There is no p or q: metapath walks are first-order and follow the schema, not a biased return/explore rule.

Usage

params_metapath2vec(
  walks_per_node = 40L,
  walk_length = 40L,
  n_epochs = 20L,
  n_negatives = 5L,
  window_size = 2L,
  lr = 0.01,
  sample = 0.001,
  num_workers = NULL
)

Arguments

walks_per_node

Integer. Number of random walks per node of the metapath's starting type. Defaults to 40L.

walk_length

Integer. Length of each random walk. Rounded up so the walk closes on a full number of schema cycles. Defaults to 40L.

n_epochs

Integer. Number of training epochs. Defaults to 20L.

n_negatives

Integer. Number of negative samples. Defaults to 5L.

window_size

Integer. Context window size. Defaults to 2L.

lr

Numeric. Learning rate. Defaults to 0.01.

sample

Numeric. Subsampling threshold for frequent nodes. Defaults to 0.001.

num_workers

Integer or NULL. Number of worker threads. If kept to NULL, it resolves to available cores - 2 (min 1). Defaults to NULL.

Value

A named list with the following elements:

• walks_per_node - Integer. Number of random walks per node of the metapath's starting type. Defaults to 40L.

• walk_length - Integer. Length of each random walk. Rounded up so the walk closes on a full number of schema cycles. Defaults to 40L.

• num_workers - Integer or NULL. Number of worker threads. If kept to NULL, it resolves to available cores - 2 (min 1). Defaults to NULL.

• n_epochs - Integer. Number of training epochs. Defaults to 20L.

• n_negatives - Integer. Number of negative samples. Defaults to 5L.

• window_size - Integer. Context window size. Defaults to 2L.

• lr - Numeric. Learning rate. Defaults to 0.01.

• sample - Numeric. Subsampling threshold for frequent nodes. Defaults to 0.001.