GreedyMerge: GreedyMerge
In GreedySBTM: Greedy Stochastic Block Transition Models

Description Usage Arguments Value References See Also Examples

Runs the hierarchical clustering algorithm to improve the solution obtained through GreedyICL.

1	GreedyMerge(adj_cube, allocations, verbose = FALSE)

`adj_cube`	A binary array of size `NxNxT` representing the dynamic network. The generic entry in position `[i,j,t]` is equal to `1` if `i` interacts with `j` during the `t`-th time frame, or to `0` otherwise. Only undirected networks with no self-edges are supported, so each slice of the array must be a symmetric matrix with null elements on the diagonal.
`allocations`	Initial allocations used by the algorithm. This should be a matrix of size `TxN` denoting the cluster membership of each node at each time. Values should be strictly positive integers. The value zero should be used to identify inactive nodes.
`verbose`	TRUE or FALSE, indicating whether a lenghty output should be printed out. Defaults to FALSE

`computing_time`	Number of seconds required to run the function.
`icl_start`	Exact log-ICL value for the initial allocations.
`icl_end`	Exact log-ICL value for the optimal allocations found.
`allocations`	Optimal allocations.

Rastelli, R. (2017) "Exact integrated completed likelihood maximisation in a stochastic block transition model for dynamic networks", https://arxiv.org/abs/1710.03551

GreedyICL, CollapseLabels.

set.seed(12345)
data(reality_mining_84)
tframes <- dim(reality_mining_84)[3]
N <- dim(reality_mining_84)[1]

### Evaluate matrix containing the degrees of each node at each time
degrees <- apply(X = reality_mining_84, MARGIN = c(1,3), FUN = sum)

### create a binary matrix with ones corresponding to inactive nodes
inactive_nodes <- degrees == 0

### create an edgelist listing all of the ids (time, node_label) of inactive nodes 
n_inactive_nodes <- sum(inactive_nodes)
list_of_inactive_nodes <- matrix(NA,n_inactive_nodes,2)
index <- 1
for (t in 1:tframes) for (i in 1:N)  if (inactive_nodes[i,t])
{
  list_of_inactive_nodes[index,1] = t
  list_of_inactive_nodes[index,2] = i
  index = index + 1
}

### Find starting allocations using k-means
allocations_init <- GreedyInit(reality_mining_84, 20, list_of_inactive_nodes)

### Run the GreedyICL algorithm (this may take some time)
output_greedy <- GreedyICL(reality_mining_84, allocations_init, max_n_iter = 1)
### max_n_iter is set to 1 to speed up the demonstration: 
### please always use max_n_iter = 100 in applications

### Run the hierarchical clustering routine
output_merge <- GreedyMerge(adj_cube = reality_mining_84, allocations = output_greedy$allocations)
str(output_merge)