![[Experimental]](figures/lifecycle-experimental.svg)
The function gives a name to networks clusters. It also gives the edges the name of their cluster. The clusters are named according to the column chosen by the user (for instance, in the case nodes are articles, the name may be the author and date of an article).
Usage
name_clusters(
graphs,
method = c("tidygraph_functions", "given_column", "tf-idf"),
name_merged_clusters = FALSE,
cluster_id,
label_columns,
label_name = "cluster_label",
tidygraph_function = NULL,
order_by = NULL,
text_columns = NULL,
nb_terms_label = 3,
...
)Arguments
- graphs
A tibble graph (from tidygraph) or a list of tibble graphs.
- method
The method for finding the names, among
tidygraph_functions,given_column, andtf-idf(see the details). Thetf-idfmethod is chosen by default.- name_merged_clusters
Set to
TRUEif your clusters have been established for all your tibble graphs and thus are unique. Typically, you have such clusters after runningmerge_dynamic_clusters().- cluster_id
The column you want to name. Generally, the column with the identifier of the clusters, whether the simple cluster detected with
add_clusters()or the merged clusters detected withmerge_dynamic_clusters().- label_columns
The column you want to be used to name the clusters. If the nodes are article, you can choose, for instance, the columns with the author of the article and the date of publication.
- label_name
The name of the column with cluster names, that will be created by the function. "cluster_label" by default.
- tidygraph_function
For the
tidygraph_functionsmethod (see the details), the centrality measure to be chosen among the measures implemented in intidygraph(seetidygraph::centrality()).- order_by
For the
given_columnmethod, the column within the nodes list of your tibble graph(s) you want to be used to classify nodes and choose names. This must be a numeric column. For instance, you can use thenode_sizecolumn of your network if you have setcompute_sizetoTRUEinbuild_network()orbuild_dynamic_networks().- text_columns
For the
tf-idfmethod, the columns with the text you want to analyze. If you give multiple columns, they will be united to extract the terms. This is a parameter ofextract_tfidf().- nb_terms_label
For the
tf-idfmethod, the number of terms you want to be used to serve a the name of a cluster. Terms will be separated by a comma. This is a parameter ofextract_tfidf().- ...
Additional arguments from
extract_tfidf, outside of those referred above as well as ofgrouping_across_listwhich is not relevant here.
Value
The same tibble graph or list of tibble graphs with a new column with the
names of the clusters, for both nodes and edges. If you choose the tidygraph_functions method,
the function also returns for nodes a column with the centrality measure computed.
Details
The node to be used for naming the community is chosen depending on 3 methods:
the
tidygraph_functionsmethod: the name of a cluster comes from the node, within the cluster, which has the highest centrality measure. The user can choose the different centrality measure implemented intidygraph(seetidygraph::centrality()for details).the
given_columnmethod: the user gives a column of the tibble graph(s), with numeric values, that will be used to classify the nodes and choose the name of each cluster. Thelabel_columnsof the node with the highest numerical value in the cluster will be used to name the cluster.the
tf-idfmethod: clusters are name according to the terms with the highest tf-idf value for each cluster. The user furnishes one or several columns with text, and the function extracts the term and calculate the tf-idf value of each term, depending on all the clusters. This method usesextract_tfidf().
Please note that, when name_merged_clusters is set to FALSE, the TF-IDF is computed
tibble graph by tibble graph. It means that it is more likely that clusters in different
tibble graphs will share the same name.
Examples
library(networkflow)
nodes <- Nodes_stagflation |>
dplyr::rename(ID_Art = ItemID_Ref) |>
dplyr::filter(Type == "Stagflation")
references <- Ref_stagflation |>
dplyr::rename(ID_Art = Citing_ItemID_Ref)
temporal_networks <- build_dynamic_networks(nodes = nodes,
directed_edges = references,
source_id = "ID_Art",
target_id = "ItemID_Ref",
time_variable = "Year",
cooccurrence_method = "coupling_similarity",
time_window = 20,
edges_threshold = 1,
overlapping_window = TRUE,
filter_components = TRUE,
verbose = FALSE)
temporal_networks <- add_clusters(temporal_networks,
objective_function = "modularity",
clustering_method = "leiden",
verbose = FALSE)
# You can name the clusters in each tibble graphs:
temporal_networks_with_names <- name_clusters(graphs = temporal_networks,
method = "tidygraph_functions",
name_merged_clusters = FALSE,
cluster_id = "cluster_leiden",
label_columns = c("Author", "Year"),
tidygraph_function = tidygraph::centrality_pagerank())
temporal_networks_with_names[[1]]
#> # A tbl_graph: 74 nodes and 446 edges
#> #
#> # An undirected simple graph with 1 component
#> #
#> # Edge Data: 446 × 9 (active)
#> from to weight Source Target cluster_leiden_from cluster_leiden_to
#> <int> <int> <dbl> <chr> <chr> <chr> <chr>
#> 1 6 11 0.00158 1021902 1111111122 02 02
#> 2 6 45 0.000173 1021902 1111111128 02 03
#> 3 6 66 0.000430 1021902 1111111134 02 03
#> 4 6 35 0.000644 1021902 1111111146 02 02
#> 5 6 20 0.000126 1021902 1111111180 02 02
#> 6 6 42 0.000614 1021902 1111111182 02 02
#> 7 6 21 0.000343 1021902 1111111183 02 02
#> 8 6 53 0.000259 1021902 1184127 02 03
#> 9 6 31 0.00121 1021902 14490177 02 02
#> 10 6 65 0.000274 1021902 16167977 02 03
#> # ℹ 436 more rows
#> # ℹ 2 more variables: cluster_leiden <chr>, cluster_label <chr>
#> #
#> # Node Data: 74 × 12
#> ID_Art Author Year Author_date Title Journal Type time_window cluster_leiden
#> <chr> <chr> <int> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 16182… GORDO… 1975 GORDON-R-1… ALTE… BROOKI… Stag… 1975-1994 01
#> 2 26283… GORDO… 1975 GORDON-R-1… THE … BROOKI… Stag… 1975-1994 01
#> 3 16182… OKUN-A 1975 OKUN-A-197… INFL… BROOKI… Stag… 1975-1994 02
#> # ℹ 71 more rows
#> # ℹ 3 more variables: size_cluster_leiden <dbl>, centrality_pagerank <dbl>,
#> # cluster_label <chr>
# Or you can name the dynamic clusters:
temporal_networks <- merge_dynamic_clusters(temporal_networks,
cluster_id = "cluster_leiden",
node_id = "ID_Art",
threshold_similarity = 0.51,
similarity_type = "partial")
temporal_networks_with_names <- name_clusters(graphs = temporal_networks,
method = "tf-idf",
name_merged_clusters = TRUE,
cluster_id = "dynamic_cluster_leiden",
text_columns = "Title",
nb_terms_label = 5,
clean_word_method = "lemmatise")
#> Warning: A shallow copy of this data.table was taken so that := can add or remove 2 columns by reference. At an earlier point, this data.table was copied by R (or was created manually using structure() or similar). Avoid names<- and attr<- which in R currently (and oddly) may copy the whole data.table. Use set* syntax instead to avoid copying: ?set, ?setnames and ?setattr. It's also not unusual for data.table-agnostic packages to produce tables affected by this issue. If this message doesn't help, please report your use case to the data.table issue tracker so the root cause can be fixed or this message improved.
temporal_networks_with_names[[1]]
#> # A tbl_graph: 74 nodes and 446 edges
#> #
#> # An undirected simple graph with 1 component
#> #
#> # Edge Data: 446 × 12 (active)
#> from to weight Source Target cluster_leiden_from cluster_leiden_to
#> <int> <int> <dbl> <chr> <chr> <chr> <chr>
#> 1 6 11 0.00158 1021902 1111111122 02 02
#> 2 6 45 0.000173 1021902 1111111128 02 03
#> 3 6 66 0.000430 1021902 1111111134 02 03
#> 4 6 35 0.000644 1021902 1111111146 02 02
#> 5 6 20 0.000126 1021902 1111111180 02 02
#> 6 6 42 0.000614 1021902 1111111182 02 02
#> 7 6 21 0.000343 1021902 1111111183 02 02
#> 8 6 53 0.000259 1021902 1184127 02 03
#> 9 6 31 0.00121 1021902 14490177 02 02
#> 10 6 65 0.000274 1021902 16167977 02 03
#> # ℹ 436 more rows
#> # ℹ 5 more variables: cluster_leiden <chr>, dynamic_cluster_leiden_from <chr>,
#> # dynamic_cluster_leiden_to <chr>, dynamic_cluster_leiden <chr>,
#> # cluster_label <chr>
#> #
#> # Node Data: 74 × 12
#> ID_Art Author Year Author_date Title Journal Type time_window cluster_leiden
#> <chr> <chr> <int> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 16182… GORDO… 1975 GORDON-R-1… ALTE… BROOKI… Stag… 1975-1994 01
#> 2 26283… GORDO… 1975 GORDON-R-1… THE … BROOKI… Stag… 1975-1994 01
#> 3 16182… OKUN-A 1975 OKUN-A-197… INFL… BROOKI… Stag… 1975-1994 02
#> # ℹ 71 more rows
#> # ℹ 3 more variables: size_cluster_leiden <dbl>, dynamic_cluster_leiden <chr>,
#> # cluster_label <chr>