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Main path extraction from a citation network

Source: R/main_path.R
main_path.Rd

Extracts the main path or key-route network from a directed citation adjacency matrix using traversal weights (SPC, SPLC, or SPNP).

Usage

main_path(
  A,
  weights = NULL,
  method = c("global", "local", "key_route"),
  weight_type = c("spc", "splc", "spnp"),
  k = 1L,
  seeds = NULL
)

Arguments

A

A square, named, directed adjacency matrix in which A[i,j] > 0 means that paper j cites paper i, so that the paths follow the flow of knowledge. See dag.

weights

Output of traversal_weights(). Computed internally with weight_type when NULL.

method

One of "global" (default), "local", or "key_route".

weight_type

One of "spc" (default), "splc", or "spnp". Ignored when weights is supplied.

k

Integer number of seed routes for method = "key_route". Default 1L.

seeds

Character vector of seed node names for method = "local".

Value

A named list:

nodes

Character vector of node names on the main path or key-route network (union across all routes).

edges

Square adjacency matrix restricted to path nodes and edges, with the same values as A for included edges and zero elsewhere.

routes

List of character vectors, one per extracted route, each giving the ordered sequence of node names.

weights

The traversal_weights() result used.

Details

Three extraction strategies are available:

"global"

Finds the single source-to-sink path that maximises the total accumulated edge weight, using dynamic programming along the topological order.

"local"

Traces backward and forward from each node in seeds, always following the edge with the highest weight. Returns one route per seed.

"key_route"

Ranks all edges by weight descending; for each of the top k seed edges not yet covered by a previous route, traces backward from the edge's tail and forward from the edge's head. The union of all routes forms a sub-DAG capturing multiple intellectual trajectories (Liu & Lu, 2012).

References

Hummon, N.P. and Doreian, P. (1989). Connectivity in a citation network: The development of DNA theory. Social Networks. 11(1): 39-63. doi:10.1016/0378-8733(89)90017-8 .

Liu, J.S. and Lu, L.Y.Y. (2012). An integrated approach for main path analysis: Development of the Hirsch index as an example. Journal of the American Society for Information Science and Technology. 63(3): 528-542. doi:10.1002/asi.21692 .

Lucio-Arias, D. and Leydesdorff, L. (2008). Main-path analysis and path-dependent transitions in HistCite-based historiographs. Journal of the American Society for Information Science and Technology. 59(12): 1948-1962. doi:10.1002/asi.20903 .

Verspagen, B. (2007). Mapping technological trajectories as patent citation networks. Advances in Complex Systems. 10(1): 93-115. doi:10.1142/S0219525907000945 .

Author

Alejandro Espinosa-Rada

Examples

A <- matrix(c(
  0, 1, 1, 0, 0, 0,
  0, 0, 0, 1, 1, 0,
  0, 0, 0, 0, 1, 0,
  0, 0, 0, 0, 0, 0,
  0, 0, 0, 0, 0, 1,
  0, 0, 0, 0, 0, 0
), byrow = TRUE, nrow = 6)
rownames(A) <- letters[1:6]
colnames(A) <- letters[1:6]

mp <- main_path(A, method = "global")
mp$routes
#> [[1]]
#> [1] "a" "b" "e" "f"
#> 

kr <- main_path(A, method = "key_route", k = 2L)
kr$nodes
#> [1] "a" "b" "d" "e" "f"