br_hits {birankr}

HITS Ranks

Description

Estimate HITS 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_hits(
  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 HITS. 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 HITS. 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 HITS 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

Although originally designed for estimating ranks in unipartite graphs, HITS (Hyperlink-Induced Topic Search) is also one of the earliest bipartite ranking algorithms. Created by Jon Kleinberg (2009) doi: 10.1145/324133.324140 as an alternative to PageRank, HITS takes better account of the topology of bipartite networks by iteratively ranking nodes according to their role as an "Authority" and as a "Hub". Nodes with authority have high indegree from high ranking hubs; high ranking hubs have high outdegree to nodes with high authority. This function provides a slightly expanded version of HITS that only interfaces with bipartite networks and that allows for weighted edges. In general, HITS ranks tend to be more sensitive to user query than PageRanks, but HITS is substantially less efficient in ranking large graphs. HITS is likely less preferable than the other bipartite ranking algorithms in most applications. There are a number of contexts where HITS performs poorly, such as in graphs with extreme outliers.

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

Jon M. Kleinberg. "Authoritative sources in a hyperlinked environment". J. ACM, 46(5):604-632, September 1999.

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 HITS ranks
    HITS <- br_hits(data = df)