Wrapper function for the GeneWalk-specific node2vec parameters

Usage

params_genewalk(
  p = 1,
  q = 1,
  walks_per_node = 100L,
  walk_length = 10L,
  batch_size = 256L,
  n_epochs = 5L,
  n_negatives = 5L,
  window_size = 1L,
  lr = 0.01,
  num_workers = 1L
)

Arguments

p: Numeric. Return parameter for biased random walks. Defaults to 1.0.
q: Numeric. In-out parameter for biased random walks. Defaults to 1.0.
walks_per_node: Integer. Number of random walks per node. Defaults to 100L.
walk_length: Integer. Length of each random walk. Defaults to 10L.
batch_size: Integer. Batch size for training. Defaults to 256L.
n_epochs: Integer. Number of training epochs. Defaults to 5L.
n_negatives: Integer. Number of negative samples. Defaults to 5L.
window_size: Integer. Context window size. For GeneWalk you want embeddings from direct neighbours. Defaults to 1L.
lr: Numeric. Learning rate. Defaults to 0.01.
num_workers: Integer. Number of worker threads. Anything above 1L makes the fitting much faster, at the cost of reproducibility. Defaults to 1L.

Value

A named list with the following elements:

p - Numeric. Return parameter for biased random walks. Defaults to 1.0.
q - Numeric. In-out parameter for biased random walks. Defaults to 1.0.
walks_per_node - Integer. Number of random walks per node. Defaults to 100L.
walk_length - Integer. Length of each random walk. Defaults to 10L.
num_workers - Integer. Number of worker threads. Anything above 1L makes the fitting much faster, at the cost of reproducibility. Defaults to 1L.
batch_size - Integer. Batch size for training. Defaults to 256L.
n_epochs - Integer. Number of training epochs. Defaults to 5L.
n_negatives - Integer. Number of negative samples. Defaults to 5L.
window_size - Integer. Context window size. For GeneWalk you want embeddings from direct neighbours. Defaults to 1L.
lr - Numeric. Learning rate. Defaults to 0.01.