ednn {PLEXI}

Encoder decoder neural network (EDNN) function

Description

Encoder decoder neural network (EDNN) function

Usage

ednn(
  x,
  y,
  x.test,
  embedding.size = 2,
  epochs = 10,
  batch.size = 5,
  l2reg = 0,
  demo = TRUE,
  verbose = FALSE
)

Arguments

x

concatenated adjacency matrices for different layers containing the nodes in training phase

y

concatenated random walk probability matrices for different layers containing the nodes in training phase

x.test

concatenated adjacency matrices for different layers containing the nodes in test phase. Can be = X for transductive inference.

embedding.size

the dimension of embedding space, equal to the number of the bottleneck hidden nodes.

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).

demo

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

verbose

if TRUE a progress bar is shown.

Value

The embedding space for x.test.

Examples

myNet = network_gen(n.nodes = 50)
graphData = myNet[["data_graph"]]
edge.list = graphData[,1:2]
edge.weight = graphData[,3:4]
XY = ednn_io_prepare(edge.list, edge.weight)
X = XY[["X"]]
Y = XY[["Y"]]
embeddingSpace = ednn(x = X, y = Y, x.test = X)