sirir function

SIR-based Influence Ranking

This function is achieved by the integration susceptible-infected-recovered (SIR) model with the leave-one-out cross validation technique and ranks network nodes based on their true universal influence. One of the applications of this function is the assessment of performance of a novel algorithm in identification of network influential nodes by considering the SIRIR ranks as the ground truth (gold standard).

sirir(
  graph,
  vertices = V(graph),
  beta = 0.5,
  gamma = 1,
  no.sim = 100,
  ncores = "default",
  seed = 1234,
  loop_verbose = TRUE,
  node_verbose = FALSE
)

Arguments

• graph: A graph (network) of the igraph class.
• vertices: A vector of desired vertices, which could be obtained by the V function.
• beta: Non-negative scalar. The rate of infection of an individual that is susceptible and has a single infected neighbor. The infection rate of a susceptible individual with n infected neighbors is n times beta. Formally this is the rate parameter of an exponential distribution.
• gamma: Positive scalar. The rate of recovery of an infected individual. Formally, this is the rate parameter of an exponential distribution.
• no.sim: Integer scalar, the number of simulation runs to perform SIR model on the original network as well as perturbed networks generated by leave-one-out technique. You may choose a different no.sim based on the available memory on your system.
• ncores: Integer; the number of cores to be used for parallel processing. If ncores == "default" (default), the number of cores to be used will be the max(number of available cores) - 1. We recommend leaving ncores argument as is (ncores = "default").
• seed: A single value, interpreted as an integer to be used for random number generation.
• loop_verbose: Logical; whether the accomplishment of the evaluation of network nodes in each loop should be printed (default is TRUE).
• node_verbose: Logical; whether the process of Parallel Socket Cluster creation should be printed (default is FALSE).

Returns

A two-column dataframe; a column containing the difference values of the original and perturbed networks and a column containing node influence rankings

Examples

## Not run:

set.seed(1234)
My_graph <- igraph::sample_gnp(n=50, p=0.05)
GraphVertices <- V(My_graph)
Influence.Ranks <- sirir(graph = My_graph, vertices = GraphVertices, 
                         beta = 0.5, gamma = 1, ncores = "default", no.sim = 10, seed = 1234)
## End(Not run)

See Also

cent_network.vis, and sir for a complete description on SIR model

Other centrality functions: betweenness(), clusterRank(), collective.influence(), h_index(), lh_index(), neighborhood.connectivity()