Compute Network Topology and Layout Coordinates

Calculates the 2D layout coordinates and key centrality metrics for a complex network. This function serves as the final step in preparing network data for visualization.

Usage

computeMapTopology(nodeAttributes, network, verbose = TRUE)

Arguments

nodeAttributes

A `tibble` or `data.frame` containing attributes for each node (complex). Must contain a column with node identifiers that matches the source/target columns in the `network` data.

network

A `tibble` or `data.frame` representing the network edges. Must contain columns for source, target, and edge `weight`.

verbose

A logical value indicating whether to print progress messages. Defaults to `TRUE`.

Value

A `tibble` containing all original columns from `nodeAttributes` plus four new columns: `x`, `y` (layout coordinates), `betweenness`, and `degree`. The table is arranged in descending order of betweenness and degree.

Details

This function takes a node attribute table and an edge list (network) and performs the following steps:

1. Constructs an `igraph` graph object from the provided data.

2. Computes a force-directed layout using the Fruchterman-Reingold algorithm via `ggraph::create_layout`. Edge weights are used to influence the layout, pulling strongly connected nodes closer together.

3. Calculates node centrality metrics:

- **Betweenness Centrality:** Measures how often a node lies on the shortest path between other nodes (normalized).

- **Degree Centrality:** The number of edges connected to a node.

4. Merges the layout coordinates and centrality scores back into the original node attribute table.

See also

`generateNodeAttributes()`, `buildComplexNetwork()`

Author

Qingzhou Zhang <zqzneptune@hotmail.com>

Examples

# --- Sample Data ---
# 1. Node attributes
nodes <- tibble::tibble(
  complexId = c("Cpx1", "Cpx2", "Cpx3"),
  proteinCount = c(10, 8, 12)
)

# 2. Network edges
net <- tibble::tibble(
  source = c("Cpx1", "Cpx2"),
  target = c("Cpx2", "Cpx3"),
  weight = c(0.8, 0.6)
)

# --- Compute Topology ---
masterLayout <- computeMapTopology(nodes, net)
#> Computing map topology (layout and centrality)...
#> Topology computation complete.
print(masterLayout)
#> # A tibble: 3 × 6
#>   complexId proteinCount      x     y betweenness degree
#>   <chr>            <dbl>  <dbl> <dbl>       <dbl>  <dbl>
#> 1 Cpx2                 8 -0.181 1.27            1      2
#> 2 Cpx1                10  0.971 1.69            0      1
#> 3 Cpx3                12 -1.47  0.805           0      1