mnda_embedding {mnda}

Calculate the embedding space for a multiplex network

Description

Calculate the embedding space for a multiplex network

Usage

mnda_embedding(
  graph.data,
  outcome,
  indv.index = NULL,
  edge.threshold = 0,
  train.rep = 50,
  embedding.size = 2,
  epochs = 10,
  batch.size = 5,
  l2reg = 0,
  walk.rep = 100,
  n.steps = 5,
  random.walk = TRUE,
  demo = TRUE,
  verbose = TRUE
)

Arguments

graph.data

dataframe of the graph data containing edge list and edge weights. column 1 and 2 consisting of the edge list (undirected). column 3 and 4 consisting the edge weights corresponding to each graph, respectively.

outcome

a vector of outcomes for each network.

indv.index

the index of individual networks.

edge.threshold

numeric value to set edge weights below the threshold to zero (default: 0). the greater edge weights do not change.

train.rep

numeric value to set the number of EDNN training repeats (default: 50).

embedding.size

the dimension of embedding space, equal to the number of the bottleneck hidden nodes (default: 5).

epochs

maximum number of pocks. An early stopping callback with a patience of 5 has been set inside the function (default = 10).

batch.size

batch size for learning (default = 5).

l2reg

the coefficient of L2 regularization for the input layer (default = 0).

walk.rep

number of repeats for the random walk (default: 100).

n.steps

number of the random walk steps (default: 5).

random.walk

boolean value to enable the random walk algorithm (default: TRUE).

demo

a boolean vector to indicate this is a demo example or not

verbose

if TRUE a progress bar is shown.

Value

a list of embedding spaces for each node.

Examples

myNet = network_gen(N_nodes = 50, N_var_nodes = 5, N_var_nei = 40, noise_sd = .01)
graph_data = myNet[["data_graph"]]
embeddingSpaceList = mnda_embedding(graph.data=graph_data, outcome=c(1,2),
train.rep=2, random.walk=FALSE)