Implement rewiring algorithms to current empirical networks to randomize certain network properties, while keeping other characterisitics constant (such as preserved degree sequence). These null networks can them be used to compare against and normalize the empirical networks.
rewire_network.RdNote that this essentially erases edge metrics and treats networks like binary graphs. Edge weights are not used in calculating network topology metrics.
Usage
rewire_network(
e,
network_name,
channels = "cfos",
method = "ms",
ontology = "unified",
n_rewires = 10000,
n_networks = 100,
return_graphs = FALSE,
seed = 5
)Arguments
- e
experiment object
- network_name
(str) Name of the network
- channels
(str) Vector of channels to process
- method
(str, default = "ms") "ms" implements Maslov-Sneppen rewiring approach (annuls all network properties except for network size, connection density, and degree distribution).
- ontology
(str, default = "allen") Region ontology to use. options = "allen" or "unified"
- n_rewires
(int, default = 10000) The number of rewires for randomization for "ms" rewiring implementation. Recommended to be the larger of either 10,000 or 10*No. edges in a graph.
- n_networks
(int, default = 100) The number of random networks to create
- return_graphs
(logical, default = FALSE) if TRUE, returns a list organized by channel containing a sublist, with each element containing a tidygraph object. This must be FALSE if you want to run you want to summarize the null network statistics with
summarize_null_networks()- seed
(int, default = 5) Random seed for future replication.