network {ECoL}  R Documentation 
Classification task. The network measures represent the dataset as a graph and extract structural information from it. The transformation between raw data and the graph representation is based on the epsilonNN algorithm. Next, a postprocessing step is applied to the graph, pruning edges between examples of opposite classes.
network(...)
## Default S3 method:
network(x, y, measures = "all", eps = 0.15,
summary = c("mean", "sd"), ...)
## S3 method for class 'formula'
network(formula, data, measures = "all", eps = 0.15,
summary = c("mean", "sd"), ...)
... 
Not used. 
x 
A data.frame contained only the input attributes. 
y 
A factor response vector with one label for each row/component of x. 
measures 
A list of measures names or 
eps 
The percentage of nodes in the graph to be connected. 
summary 
A list of summarization functions or empty for all values. See
summarization method to more information. (Default:

formula 
A formula to define the class column. 
data 
A data.frame dataset contained the input attributes and class. 
The following measures are allowed for this method:
Average Density of the network (Density) represents the number of edges in the graph, divided by the maximum number of edges between pairs of data points.
Clustering coefficient (ClsCoef) averages the clustering tendency of the vertexes by the ratio of existent edges between its neighbors and the total number of edges that could possibly exist between them.
Hubs score (Hubs) is given by the number of connections it has to other nodes, weighted by the number of connections these neighbors have.
A list named by the requested network measure.
Gleison Morais and Ronaldo C Prati. (2013). Complex Network Measures for Data Set Characterization. In 2nd Brazilian Conference on Intelligent Systems (BRACIS). 12–18.
Luis P F Garcia, Andre C P L F de Carvalho and Ana C Lorena. (2015). Effect of label noise in the complexity of classification problems. Neurocomputing 160, 108–119.
Other complexitymeasures: balance
,
correlation
, dimensionality
,
linearity
, neighborhood
,
overlapping
, smoothness
## Extract all network measures for classification task
data(iris)
network(Species ~ ., iris)