R/origin_methods.r
origin_multiple.Rd
Multiple origin estimation using community partitioning
origin_multiple( events, type = c("edm", "backtracking", "centrality"), graph, no = 2, distance, fast = TRUE, ... )
events | numeric vector of event counts at specific time point |
---|---|
type | character specifying the method, |
graph | igraph object specifying the underlying network graph |
no | numeric specifying the number of supposed origins |
distance | numeric matrix specifying the distance matrix |
fast | logical specifying community partitioning algorithm, default is |
... | parameters to be passed to origin methods |
origin_multiple
returns an list object with objects of class origin
of length no
Zang, W., Zhang, P., Zhou, C. and Guo, L. (2014) Discovering Multiple Diffusion Source Nodes in Social Networks. Procedia Computer Science, 29, 443-452. <DOI: 10.1016/j.procs.2014.05.040>
Other origin-est:
origin()
data(ptnAth) # backtracking origin_multiple(events=delayAth[10,-c(1:2)], type='backtracking', graph=ptnAth, no=2)#> [[1]] #> Backtracking origin estimation: #> #> estimated node of origin 6: 6 #> #> [[2]] #> Backtracking origin estimation: #> #> estimated node of origin 1: 1 #># edm athnet <- igraph::as_adjacency_matrix(ptnAth, sparse=FALSE) p <- athnet/rowSums(athnet) eff <- eff_dist(p)#> Computing the effective distance between 51 nodes: #> 1...................................................done#> [[1]] #> Effective distance median origin estimation: #> #> estimated node of origin 3: 3 #> #> [[2]] #> Effective distance median origin estimation: #> #> estimated node of origin 2: 2 #>