Skip to contents

Relations between topics: a weighted network of shared vocabulary

Source: R/text_verbs.R
hg_relations.Rd

For a clustered document hypergraph, builds the topic-by-topic co-occurrence network of the bibliometric kind: the strength between two topics is the sum, over all words, of the product of the number of documents in each topic that contain the word (full counting, the aggregation bibnets uses for keyword co-occurrence). The raw sum can be normalised by the same similarity measures as bibnets' normalize(), with the diagonal of the co-occurrence matrix as each topic's total. The result is an edge list with source, target and weight, the three columns Gephi reads, or a cograph_network for plotting.

Usage

hg_relations(
  hg,
  clusters,
  similarity = c("none", "association", "cosine", "jaccard", "inclusion", "equivalence"),
  what = c("edges", "network")
)

Arguments

hg

The document hypergraph the clustering was computed on.

clusters

The tidy table returned by hg_cluster() (columns node, cluster), or a named vector of cluster labels.

similarity

"none" (default, the raw sum), "association", "cosine", "jaccard", "inclusion" or "equivalence".

what

"edges" (default) for the edge list, "network" for a cograph_network built from it with cograph::as_cograph(), whose node table carries each topic's size.

Value

For what = "edges": a base data.frame, one row per pair of topics with a positive weight, columns source, target, weight, pairs in the topics' natural order. For what = "network": a cograph_network with one node per topic (label, name, size) and one undirected weighted edge per pair. Raises hypernets_bad_input for unknown node names.

Details

With D_i the diagonal entry of topic i and A_ij the raw sum: "association" is A_ij / (D_i D_j), "cosine" is A_ij / sqrt(D_i D_j), "jaccard" is A_ij / (D_i + D_j - A_ij), "inclusion" is A_ij / min(D_i, D_j) and "equivalence" is A_ij^2 / (D_i D_j).

References

van Eck, N. J., & Waltman, L. (2009). How to normalize cooccurrence data? An analysis of some well-known similarity measures. Journal of the American Society for Information Science and Technology, 60(8), 1635–1651.

Examples

hg <- text_hypergraph(c(
  cooking_1 = "simmer the soup with onions and carrots",
  cooking_2 = "this soup recipe needs salt on a cold night",
  space_1 = "the telescope revealed a distant galaxy and stars",
  space_2 = "astronomers aimed the telescope at the stars all night"
), stop_words = c("the", "with", "and", "a", "this", "at", "on", "all"))
topics <- hg_cluster(hg, k = 2, seed = 1)
hg_relations(hg, topics)
#>      source    target weight
#> 1 Cluster 1 Cluster 2      1
hg_relations(hg, topics, similarity = "cosine")
#>      source    target     weight
#> 1 Cluster 1 Cluster 2 0.07715167