Build a single network from bipartite links

build_network() projects a two-mode relation (e.g. articles and the references they cite) onto a one-mode network of the source entities, for the whole corpus at once. It is build_dynamic_networks() without time windows: see that function for the measures, the thresholds and the literature behind them.

Usage

build_network(
  nodes,
  directed_edges,
  source_id,
  target_id,
  document_id = NULL,
  projection_on = c("source", "target"),
  method,
  edges_threshold = 1,
  alpha = NULL,
  backbone_args = list(),
  compute_size = FALSE,
  keep_singleton = FALSE,
  filter_components = FALSE,
  ...,
  verbose = TRUE
)

Arguments

nodes

A data frame with one row per source entity (e.g. one row per article) and its metadata (authors, year, title...). Not modified.

directed_edges

A data frame of bipartite links, one row per source_id -> target_id link (e.g. article -> cited reference). Duplicated links are counted once. Not modified.

source_id

Character scalar, the column identifying the entities that become the nodes of the network. It must exist in both nodes and directed_edges, with one row per value in nodes.

target_id

Character scalar, the column of directed_edges with the entities that create the links (e.g. references).

document_id

Optional column of directed_edges identifying the document through which each link exists. With it, source_id entities are not documents but entities that publish documents (authors, journals, institutions) and a reference cited by several documents of an entity counts several times, following biblionetwork::entity_network(). Only for the similarity measures.

projection_on

"source" (default) links the source_id entities that share target_id entities (bibliographic coupling: articles sharing references). "target" links the target_id entities that share source_id entities (co-citation: cited documents linked when they are cited by the same documents). With "target", nodes is keyed by target_id.

method

Required, no default. How the ties between sources are built; the origin of each method is given in the details. One of

the similarity measures of biblionetwork (biblionetwork::biblio_coupling(method = ), or biblionetwork::entity_network() with document_id): "angle", "strength", "similarity", "jaccard", "vladutz";
the null models of the statistical backbones of backbone::backbone_from_projection(): "sdsm", "fdsm", "fixedfill", "fixedrow", "fixedcol". They need alpha.

edges_threshold

For the similarity measures, the minimum number of shared targets for two sources to be linked (1 by default). Ignored by the backbone methods.

alpha

For the backbone methods, the significance level of the test of each tie (e.g. 0.05). Lower values keep fewer edges. Required for them, ignored by the similarity measures.

backbone_args

Optional list of further arguments for backbone::backbone_from_projection() (e.g. mtc = "holm" to correct for multiple tests). alpha given here overrides the argument of the function.

compute_size

Logical. If TRUE, adds a node_size column: the number of times each node is a target of the sources of the same window (for a coupling network, the number of citations received within the corpus). It requires nodes to contain the target_id column.

keep_singleton

Logical. If FALSE (default), nodes without any edge are removed.

filter_components

Logical. If TRUE, keeps only the main component(s) with filter_components().

...

With filter_components = TRUE, arguments of filter_components() (e.g. nb_components).

verbose

Logical. If TRUE (default), reports the steps.

Value

A tbl_graph, undirected, with the columns of nodes as node attributes and, for the structured method, a weight edge column.

Details

The cases (which arguments for which kind of network), the methods and where they come from are described in the Details of build_dynamic_networks(). With verbose = TRUE (default), the function says which method it applies.

Examples

nodes <- subset(Nodes_stagflation, source_type == "Stagflation")

network <- build_network(
  nodes = nodes,
  directed_edges = Ref_stagflation,
  source_id = "source_id",
  target_id = "target_id",
  method = "similarity",
  edges_threshold = 2
)
#> ℹ Coupling (`biblionetwork::biblio_coupling()`) with "similarity": ties
#>   weighted by the idf-weighted cosine of the two lists of targets, after Shen
#>   et al. (2019), between source_id entities sharing target_id entities.
#> ℹ Two source_id are linked when they share at least 2 target_id
#>   (`edges_threshold`).
#> ℹ Nodes without any edge are removed (`keep_singleton = FALSE`).
network
#> # A tbl_graph: 131 nodes and 1501 edges
#> #
#> # An undirected simple graph with 2 components
#> #
#> # Node Data: 131 × 7 (active)
#>    source_id source_author source_year source_label  source_title source_journal
#>    <chr>     <chr>         <chr>       <chr>         <chr>        <chr>         
#>  1 96284971  ALBANESI-S    2003        ALBANESI-S-2… EXPECTATION… "THE REVIEW O…
#>  2 37095547  ATESOGLU-H    1980        ATESOGLU-H-1… INFLATION A… "JOURNAL OF P…
#>  3 46282251  ATESOGLU-H    1982        ATESOGLU-H-1… WAGES AND S… "JOURNAL OF P…
#>  4 214927    BALL-L        1991        BALL-L-1991   THE GENESIS… "JOURNAL OF M…
#>  5 2207578   BALL-L        1995        BALL-L-1995a  RELATIVE-PR… "THE QUARTERL…
#>  6 10729971  BALL-L        1995        BALL-L-1995b  TIME-CONSIS… "JOURNAL OF M…
#>  7 95575910  BARSKY-R      2002        BARSKY-R-2002 DO WE REALL… "NBER MACROEC…
#>  8 105203318 BARSKY-R      2004        BARSKY-R-2004 OIL AND THE… "JOURNAL OF E…
#>  9 140987361 BENATI-L      2011        BENATI-L-2011 WOULD THE B… "JOURNAL OF E…
#> 10 43590636  BERNANKE-B    1997        BERNANKE-B-1… SYSTEMATIC … "BROOKINGS PA…
#> # ℹ 121 more rows
#> # ℹ 1 more variable: source_type <chr>
#> #
#> # Edge Data: 1,501 × 5
#>    from    to weight Source    Target   
#>   <int> <int>  <dbl> <chr>     <chr>    
#> 1   110   111 0.122  101742279 101742296
#> 2    98   111 0.0288 101742279 101777013
#> 3    98   110 0.0851 101742296 101777013
#> # ℹ 1,498 more rows

Usage

Arguments

Value

Details

See also

Examples