This function calculates the activation rate for each node in a graph based on its connectivity to other nodes, the *relative strength* of the connections, and a global loose factor.
The ACT spreading activation formula is represented in Equation 1: $$a(y) = \sum_x {f(x,y) \cdot a(x)} + c(y)$$, where c(y) represents the baseline activation of y. \(\alpha\) represents the proximity of a node in this network. \(t\) represents the iteration number
Usage
activation_rate(
graph,
strength,
stm,
loose = 1,
threads = 0,
remove_first = FALSE,
display_progress = TRUE
)Arguments
- graph
A square
matrix(ordgCMatrixrepresenting the background graph. Inside this adjacency matrix, each row and column of the matrix represents a node in the graph. The values of the matrix should be either 0 or 1 (or either 0 or larger than 0), where a value of 0 indicates no relations between two nodes. The diagonal of the matrix should be 0, as there are no self-edges in the graph.- strength
A vector containing the *relative strength* of connections for each node in the graph, which is the same as the last time activation rates of all nodes. The sequence is the same as the matrix.
- stm
A binary vector which indicating whether the node is activated, or in the short-term memory
- loose
A scalar numeric between 0 and 1 that determines the loose (or weight) in the calculation process.
- threads
A scalar numeric indicating the parallel threads. Default is 0 (auto-detected).
- remove_first
A logical value indicating whether or not to exclude the first node from the calculation.
- display_progress
A logical value indicating whether or not to show the progress.
Examples
library(magrittr)
#>
#> Attaching package: 'magrittr'
#> The following objects are masked from 'package:testthat':
#>
#> equals, is_less_than, not
graph <- matrix(nrow = 7, ncol=7, data=c(
0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0,
0, 1, 1, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 1, 1, 1, 0))
diag(graph) <- 0
colnames(graph) <- rownames(graph) <- seq_len(nrow(graph)) %>%
subtract(1) %>% as.character()
initial_info <- data.frame(node = colnames(graph),
strength = c(2, 4, 3, 2, 2, 1, 5),
in_stm = c(rep(1, 3), rep(0, 4)))
activation_rate(graph, initial_info$strength, initial_info$in_stm,
loose = 0.8, remove_first = TRUE)
#> Number of threads: 4, max threads: 4.
#> Solving activation patterns...
#> [1] 7.485100 5.012514 4.741765 6.746771 2.210533 5.461641