br_cohits {birankr}

CoHITS Ranks

Description

Estimate CoHITS ranks of nodes from an edge list or adjacency matrix. Returns a vector of ranks or (optionally) a list containing a vector for each mode. If the provided data is an edge list, this function returns ranks ordered by the unique values in the selected mode.

Usage

br_cohits(
  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
)

Arguments

data

Data to use for estimating CoHITS. 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 CoHITS. 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 CoHITS ranks. Defaults to "rows" (the first column of an edge list).

return_data_frame

Return results as a data frame with node names in the 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.

Details

Created by Deng, Lyo, and Kind (2009) doi: 10.1145/1557019.1557051, CoHITS was developed explicitly for use in bipartite graphs as a way to better-incorporate content information (the "Co" in CoHITS) in HITS ranks. Like HITS, CoHITS is based on a markov process for simultaneously estimating ranks across each mode of the input data. CoHITS primarily differs from HITS in that it normalizes the transition matrix by the out-degree of the source nodes, leading to an interpretation more similar to that of a random walk.

Value

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.

References

Hongbo Deng, Michael R. Lyu, and Irwin King. "A generalized co-hits algorithm and its application to bipartite graphs". In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '09, pages 239-248, New York, NY, USA, 2009. ACM.

Examples

#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 CoHITS ranks
    CoHITS <- br_cohits(data = df)