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Identify Key Routes in Citation Networks

Source: R/sniff_key_route.R
sniff_key_route.Rd

This function identifies and visualizes key citation routes within scientific networks by analyzing the most significant citation paths between publications. The algorithm implements the key-route search from the integrated main path analysis approach described in Liu & Lu (2012).

Usage

sniff_key_route(
  network,
  scope = "network",
  n_routes = 1,
  compact_gaps = FALSE,
  direction = "vertical"
)

Arguments

network

A network object of class tbl_graph or igraph containing citation data, or a list object generated by sniff_groups() when scope = "groups"

scope

Character string specifying the analysis scope. Must be either "network" (for full network analysis) or "groups" (for group-wise analysis of a grouped network)

n_routes

Positive integer specifying the number of key-route starting edges to use (default: 1). Each iteration selects the next-highest SPC edge as a new starting point and extends it into a full path. Higher values reveal auxiliary knowledge diffusion paths and divergence-convergence structures (see Liu & Lu, 2012, Figure 8).

compact_gaps

Logical. If TRUE, compresses the vertical axis by removing empty year gaps between publications. This produces shorter plots suitable for academic publications. Default is FALSE (chronological spacing).

direction

Character. Plot direction: "vertical" (default, top-to-bottom) or "horizontal" (left-to-right, oldest on the left).

Value

A list containing for each group:

  • plot - A ggplot2 object visualizing the key citation route(s)

  • data - A tibble with publication details (name, TI, AU, PY) of nodes in the key route(s)

Details

The function implements the key-route search from Liu & Lu (2012):

  1. Computes Search Path Count (SPC) for each citation link using an efficient O(V+E) algorithm based on topological sort. SPC measures how many source-to-sink paths traverse each link.

  2. Selects the key-route: the link with the highest SPC value.

  3. Searches forward from the end node of the key-route, greedily following the outgoing link with the highest SPC, until a sink is reached.

  4. Searches backward from the start node of the key-route, greedily following the incoming link with the highest SPC, until a source is reached.

When n_routes > 1, the procedure is repeated: each iteration selects the edge with the next-highest SPC as a new starting point and extends it forward and backward. Routes can share nodes and edges, producing a divergence-convergence-divergence structure that reveals how knowledge flows through multiple sub-streams.

The SPC is computed as forward[u] * backward[v] for each edge (u, v), where forward[u] counts paths from any source to u and backward[v] counts paths from v to any sink (Batagelj, 2003). This guarantees the most significant link is always included in the key-route path.

References

Liu JS, Lu LYY. 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. 2012;63(3):528-542. doi:10.1002/asi.21692

Batagelj V. Efficient algorithms for citation network analysis. University of Ljubljana, Institute of Mathematics, Physics and Mechanics, Department of Theoretical Computer Science, Preprint Series. 2003;41:897.

Examples

if (FALSE) { # \dontrun{
# Single key-route (default)
result <- sniff_key_route(my_network, scope = "network")

# Multiple key-routes for richer structure
result <- sniff_key_route(my_network, scope = "network", n_routes = 5)

# Group-wise analysis
grouped_network <- sniff_groups(data)
result <- sniff_key_route(grouped_network, scope = "groups", n_routes = 3)

# Access results for a specific group
result$group_name$plot
result$group_name$data
} # }