Description
The RandomWalkSample function takes an input graph (which is typically large)
and outputs a sample graph.
Usage
td_random_walk_sample_mle (
vertices.data = NULL,
edges.data = NULL,
target.key = NULL,
sample.rate = 0.15,
flyback.rate = 0.15,
seed = 1000,
accumulate = NULL,
vertices.data.sequence.column = NULL,
edges.data.sequence.column = NULL,
vertices.data.partition.column = NULL,
edges.data.partition.column = NULL
)
Arguments
vertices.data |
Required Argument.
Specifies the tbl_teradata containing the vertex data.
|
vertices.data.partition.column |
Required Argument.
Specifies Partition By columns for "vertices.data".
Values to this argument can be provided as a vector, if multiple
columns are used for partition.
Types: character OR vector of Strings (character)
|
edges.data |
Required Argument.
Specifies the tbl_teradata containing the edge data.
|
edges.data.partition.column |
Required Argument.
Specifies Partition By columns for "edges.data".
Values to this argument can be provided as a vector, if multiple
columns are used for partition.
Types: character OR vector of Strings (character)
|
target.key |
Required Argument.
Specifies the names of the columns in "edges.data" that identify the target
vertex of an edge. This set of columns must have the same schema as
"vertices.data.partition.column" and "edges.data.partition.column".
Types: character OR vector of Strings (character)
|
sample.rate |
Optional Argument.
Specifies the sampling rate. This value must be in the range (0, 1.0).
Default Value: 0.15
Types: numeric
|
flyback.rate |
Optional Argument.
Specifies the chance, when visiting a vertex, of flying back to the
starting vertex. This value must be in the range (0, 1.0).
Default Value: 0.15
Types: numeric
|
seed |
Optional Argument.
Specifies the seed used to generate a series of random numbers for
"sample.rate", "flyback.rate", and any random number used internally.
Specifying this value along with "vertices.data.sequence.column" and
"edges.data.sequence.column" guarantees that the function result is
repeatable on a given Vantage system.
Default Value: 1000
Types: numeric
|
accumulate |
Optional Argument.
Specifies the names of columns in "vertices.data" to copy to the
"output.vertex.table" output tbl_teradata.
Types: character OR vector of Strings (character)
|
vertices.data.sequence.column |
Optional Argument.
Specifies the vector of column(s) that uniquely identifies each row
of the input argument "vertices.data". The argument is used to ensure
deterministic results for functions which produce results that vary
from run to run.
Types: character OR vector of Strings (character)
|
edges.data.sequence.column |
Optional Argument.
Specifies the vector of column(s) that uniquely identifies each row
of the input argument "edges.data". The argument is used to ensure
deterministic results for functions which produce results that vary
from run to run.
Types: character OR vector of Strings (character)
|
Value
Function returns an object of class "td_random_walk_sample_mle" which
is a named list containing objects of class "tbl_teradata".
Named list members can be referenced directly with the "$" operator
using the following names:
output.vertex.table
-
output.edge.table
output
Examples
# Get the current context/connection.
con <- td_get_context()$connection
# Load example data.
loadExampleData("randomwalksample_example", "citvertices_2", "citedges_2")
# Create object(s) of class "tbl_teradata".
citvertices_2 <- tbl(con, "citvertices_2")
citedges_2 <- tbl(con, "citedges_2")
# Example 1 - This function takes an input graph (which is typically large) and outputs
# a sample graph that preserves graph properties as well as possible.
td_random_walk_sample_mle_out <- td_random_walk_sample_mle(vertices.data = citvertices_2,
vertices.data.partition.column = c("id"),
edges.data = citedges_2,
edges.data.partition.column = c("from_id"),
target.key = c("to_id"),
sample.rate = 0.15,
flyback.rate = 0.15,
seed = 1000
)