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. |
vertices.data.partition.column |
Required Argument. |
edges.data |
Required Argument. |
edges.data.partition.column |
Required Argument. |
target.key |
Required Argument. |
sample.rate |
Optional Argument. |
flyback.rate |
Optional Argument. |
seed |
Optional Argument. |
accumulate |
Optional Argument. |
vertices.data.sequence.column |
Optional Argument. |
edges.data.sequence.column |
Optional Argument. |
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
)