Generate the initial graph embedding

This function will generate the initial node2vec embedding with the provided parameters. In brief, it will generate random walks over the graph and fit a Skip Gram model for the number of defined epochs to generate on a per node basis an embedding vector. These will be subsequently used for the calculation of test statistics. For more details, please refer to Ietswaart, et al.

Usage

generate_initial_emb(
  object,
  embd_dim = 8L,
  n_graph = 3L,
  genewalk_params = params_genewalk(),
  directed = FALSE,
  seed = 42L,
  .verbose = TRUE
)

Arguments

object

The GeneWalk class, please see GeneWalk().

embd_dim

Integer. Size of the embedding dimensions to create. Defaults to 8L in line with the authors recommendations.

n_graph

Integer. How many initial graphs to create. Defaults to 3L.

genewalk_params

Named list. Contains the node2vec parameters. The list has 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 - Number of worker threads during to use during SGD updates. For determism, defaults to 1L.

• batch_size - Integer. Batch size for training. Defaults to 256L.

• n_epochs - Integer. Number of training epochs. Defaults to 15L.

• 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 1e-2.

directed

Boolean. Indicates if this is a directed or undirected network. Defaults to FALSE.

seed

Integer. Seed for reproducibility.

.verbose

Boolean. Controls verbosity of the function

Value

The object with the added initial embeddings.

References

Ietswaart, et al., Genome Biol, 2021