CreateDataCV: A function to create cross-validation data.

Description

A function to create cross-validation data.

Usage

CreateDataCV(data, ratio = 0.75, G = 50,  net_type = "directed",deg_thresh = 1)

Arguments

data

Matrix. A 3-column matrix whose each row contains information of one edge in the form (from_node id, to_node id, time_stamp). from_node id is the id of the source node. to_node id is the id of the destination node. time_stamp is the arrival time of the edge. from_node id and to_node id are assumed to be integers starting from $0$. time_stamp can be either numeric or string. We assume that a smaller time_stamp represents an earlier arrival time.

ratio

Numeric. Indicates the ratio of number of new edges in the learning data to that of the testing data. Default is $0.75$.

Integer. Number of bins. Default value is $50$.

net_type

String. The type of the network: "directed" or "undirected". Default is "directed".

deg_thresh

Integer. We only consider nodes with degree at least this threshold. Default value is $1$.

Value

References

1. Pham, T., Sheridan, P. & Shimodaira, H (2016). Nonparametric Estimation of the Preferential Attachment Function in Complex Networks: Evidence of Deviations from Log Linearity, Proceedings of ECCS 2014, 141-153 (Springer International Publishing) (http://dx.doi.org/10.1007/978-3-319-29228-1_13).

2. Pham T, Sheridan P, Shimodaira H (2015) PAFit: A Statistical Method for Measuring Preferential Attachment in Temporal Complex Networks. PLoS ONE 10(9): e0137796. doi:10.1371/journal.pone.0137796 (http://dx.doi.org/10.1371/journal.pone.0137796)

Examples

Run this code

library("PAFit")
data     <- GenerateNet(N = 100,m = 1,mode = 1, alpha = 1, shape = 5, rate = 5)
data_cv  <- CreateDataCV(data$graph)

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