| 350 | 350 | #' |
|
| 351 | 351 | #' # Calculating the exposure based on Structural Equivalence ------------------ |
|
| 352 | 352 | #' set.seed(113132) |
|
| 353 | - | #' graph <- rdiffnet(100, 10) |
|
| 353 | + | #' graph <- rdiffnet(100, 4) |
|
| 354 | 354 | #' |
|
| 355 | 355 | #' SE <- lapply(struct_equiv(graph), "[[", "SE") |
|
| 356 | 356 | #' SE <- lapply(SE, function(x) { |
| 378 | 378 | #' # Structral equivalence for different communities --------------------------- |
|
| 379 | 379 | #' data(medInnovationsDiffNet) |
|
| 380 | 380 | #' |
|
| 381 | + | #' # Only using 4 time slides, this is for convenience |
|
| 382 | + | #' medInnovationsDiffNet <- medInnovationsDiffNet[, , 1:4] |
|
| 383 | + | #' |
|
| 381 | 384 | #' # METHOD 1: Using the c.diffnet method: |
|
| 382 | 385 | #' |
|
| 383 | 386 | #' # Creating subsets by city |
gvegayon
