Simulate spreading activation in a network

Usage

spreadr(
  network,
  start_run,
  retention = 0.5,
  time = 10,
  threshold_to_stop = NULL,
  decay = 0,
  suppress = 0,
  include_t0 = FALSE,
  create_names = TRUE,
  never_stop = FALSE
)

Arguments

network: Adjacency matrix or igraph object representing the network in which to simulate spreading activation.
start_run: Non-empty data.frame with mandatory columns node, activation; and optional columns time. If the time column is present, activation is added to node at each time. Otherwise, the activations are added to their corresponding nodes at t = 0.
retention: Number from 0 to 1 (inclusive) or a numeric vector of such numbers of length equals number of nodes in the network. This represents the proportion of activation that remains in the node (not spread) at each time step. Then, 1 - retention of the activation at each node is spread to neighbouring nodes. If a numeric vector, retentions are assigned to nodes according to the order given by V(network) if network is an igraph object or nrow(network) if network is an adjacency matrix.
time: Positive non-zero integer, or NULL. If not NULL, the number of time steps to simulate before stopping. Otherwise, stop with the threshold_to_stop parameter.
threshold_to_stop: Number or NULL. If not NULL, stop the simulation only when all nodes have activation value less than threshold_to_stop. Otherwise, stop with the time parameter.
decay: Number from 0 to 1 (inclusive) representing the proportion of activation that is lost at each time step.
suppress: Number representing the maximum amount of activation in a node for it to be set to 0, at each time step.
include_t0: Boolean flag indicating if activation at t = 0 should be prepended to the output data.frame. This is FALSE by default for back-compatibility.
create_names: Boolean flag indicating if nodes should be automatically named (1:n, where n is the number of nodes) in case they are missing.
never_stop: Boolean flag indicating if the simulation should be stopped if there have been too many iterations (so that there might be an infinite loop).

Value

A data.frame with node, activation and time columns representing the spread of activation in the network over time.

Details

At least one of parameters time or threshold_to_stop must be non-NULL. If both are non-NULL, the simulation stops at the earliest time possible.

The simulation iterates like so: for every i in [0, time],

Spread activation from node to node
Decay the activation at each node by the proportion specified by decay
Set the activation at nodes with activation less than suppress to 0
Add the activations in start_run with time = i to their corresponding nodes
Save the activations at each node for output
Check the terminating conditions time and threshold_to_stop. If any are satisfied, terminate the simulation.

Examples

# make an adjacency matrix and randomly fill some cells with 1s
mat <- matrix(sample(c(0,1), 100, replace=TRUE), 10, 10)
diag(mat) <- 0 # remove self-loops
initial_df <- data.frame(node=1, activation=20, stringsAsFactors=FALSE)
results <- spreadr(mat, initial_df)

head(results, 10)
#>    node activation time
#> 1     1         10    1
#> 2     2          2    1
#> 3     3          0    1
#> 4     4          0    1
#> 5     5          2    1
#> 6     6          0    1
#> 7     7          0    1
#> 8     8          2    1
#> 9     9          2    1
#> 10   10          2    1
tail(results, 10)
#>     node activation time
#> 91     1   1.224604   10
#> 92     2   1.824299   10
#> 93     3   1.313552   10
#> 94     4   1.685737   10
#> 95     5   1.784116   10
#> 96     6   2.549849   10
#> 97     7   2.317747   10
#> 98     8   2.778073   10
#> 99     9   2.296980   10
#> 100   10   2.225042   10