br_birank: BiRanks
In BrianAronson/birankr: Ranking Nodes in Bipartite and Weighted Networks

Description Usage Arguments Details Value References Examples

View source: R/10_BiRank.R

Estimate BiRanks of nodes from an edge list or adjacency matrix. Returns a vector of ranks or (optionally) a list containing a vector for each mode.

br_birank(
  data,
  sender_name = NULL,
  receiver_name = NULL,
  weight_name = NULL,
  rm_weights = FALSE,
  duplicates = c("add", "remove"),
  return_mode = c("rows", "columns", "both"),
  return_data_frame = TRUE,
  alpha = 0.85,
  beta = 0.85,
  max_iter = 200,
  tol = 1e-04,
  verbose = FALSE
)

`data`	Data to use for estimating BiRank. Must contain bipartite graph data, either formatted as an edge list (class data.frame, data.table, or tibble (tbl_df)) or as an adjacency matrix (class matrix or dgCMatrix).
`sender_name`	Name of sender column. Parameter ignored if data is an adjacency matrix. Defaults to first column of edge list.
`receiver_name`	Name of sender column. Parameter ignored if data is an adjacency matrix. Defaults to the second column of edge list.
`weight_name`	Name of edge weights. Parameter ignored if data is an adjacency matrix. Defaults to edge weights = 1.
`rm_weights`	Removes edge weights from graph object before estimating BiRank. Parameter ignored if data is an edge list. Defaults to FALSE.
`duplicates`	How to treat duplicate edges if any in data. Parameter ignored if data is an adjacency matrix. If option "add" is selected, duplicated edges and corresponding edge weights are collapsed via addition. Otherwise, duplicated edges are removed and only the first instance of a duplicated edge is used. Defaults to "add".
`return_mode`	Mode for which to return BiRank ranks. Defaults to "rows" (the first column of an edge list).
`return_data_frame`	Return results as a data frame with node names in first column and ranks in the second column. If set to FALSE, the function just returns a named vector of ranks. Defaults to TRUE.
`alpha`	Dampening factor for first mode of data. Defaults to 0.85.
`beta`	Dampening factor for second mode of data. Defaults to 0.85.
`max_iter`	Maximum number of iterations to run before model fails to converge. Defaults to 200.
`tol`	Maximum tolerance of model convergence. Defaults to 1.0e-4.
`verbose`	Show the progress of this function. Defaults to FALSE.

If input data is an edge list, this function returns ranks ordered by the unique values in the supplied edge list. Data inputted as an edge list are always assumed to contain named vertex IDs rather than to reflect an index of vertex positions in a network matrix. Users who wish for their edge lists to reflect vertex indices are recommended to input their data as a matrix or as a sparse matrix.

Network isolates are assigned a value of (1 - alpha) / (n\_columns) or (1 - beta) / (n\_rows) depending on their mode in the network. These values will always be smaller than the minimum value assigned to non-isolated nodes in the given mode. However, estimates on network isolates are non-meaningful. Users are advised to treat isolates with caution.

Created by He et al. (2017) doi: 10.1109/TKDE.2016.2611584, BiRank is a highly generalizable algorithm that was developed explicitly for use in bipartite graphs. In fact, He et al.'s implementation of BiRank forms the basis of this package's implementation of all other bipartite ranking algorithms. Like every other bipartite ranking algorithm, BiRank simultaneously estimates ranks across each mode of the input data. BiRank's implementation is also highly similar to BGRM in that it symmetrically normalizes the transition matrix. BiRank differs from BGRM only in that it normalizes the transition matrix by the square-root outdegree of the source node and the square-root indegree of the target node.

A dataframe containing each node name and node rank. If return_data_frame changed to FALSE or input data is classed as an adjacency matrix, returns a vector of node ranks. Does not return node ranks for isolates.

Xiangnan He, Ming Gao, Min-Yen Kan, and Dingxian Wang. "Birank: Towards ranking on bipartite graphs". IEEE Transactions on Knowledge and Data Engineering, 29(1):57-71, 2016

#create edge list between patients and providers
    df <- data.table(
      patient_id = sample(x = 1:10000, size = 10000, replace = TRUE),
      provider_id = sample(x = 1:5000, size = 10000, replace = TRUE)
    )

#estimate BiRank ranks
    BiRank <- br_birank(data = df)