NetOrigin (version 1.0-3)

origin: Origin Estimation for Propagation Processes on Complex Networks

Description

This is the main function for origin estimation for propagation processes on complex networks. Different methods are available: effective distance median ('edm'), recursive backtracking ('backtracking'), and centrality-based source estimation ('centrality'). For details on the methodological background, we refer to the corresponding publications.

origin_edm for effective distance-median origin estimation (Manitz et al., 2016)

origin_backtracking for recursive backtracking origin estimation (Manitz et al., 2016)

origin_centrality for centrality-based origin estimation (Comin et al., 2011)

Usage

origin(events, type = c("edm", "backtracking", "centrality"), ...)
origin_edm(events, distance, silent = TRUE)
origin_backtracking(events, graph, start_with_event_node = TRUE,
  silent = TRUE)
origin_centrality(events, graph, silent = TRUE)

Arguments

events

numeric vector of event counts at a specific time point

type

character specifying the method, 'edm', 'backtracking' and 'centrality' are available.

...

parameters to be passed to origin methods origin_edm, origin_backtracking or origin_centrality

distance

numeric matrix specifying the distance matrix (for type='edm')

silent

locigal, should the messages be suppressed?

graph

igraph object specifying the underlying network graph (for type='backtracking' and type='centrality')

start_with_event_node

logical specifying whether backtracking only starts from nodes that experienced events (for type='backtracking')

Value

origin_edm returns an object of class origin, list with

• est origin estimate

• aux data.frame with auxiliary variables

  • id as node identifier,

  • events for event magnitude,

  • wmean for weighted mean,

  • wvar for weighted variance, and

  • mdist mean distance from a node to all other nodes.

• type = 'edm' effective distance median origin estimation

origin_backtracking returns an object of class origin, list with

• est origin estimate

• aux data.frame with auxiliary variables

  • id as node identifier,

  • events for event magnitude, and

  • bcount for backtracking counts, how often backtracking identifies this source node.

• type = 'backtracking' backtracking origin estimation

origin_centrality returns an object of class origin, list with

• est origin estimate

• aux data.frame with auxiliary variables

  • id as node identifier,

  • events for event magnitude, and

  • cent for node centrality (betweenness divided degree).

• type = 'centrality' centrality-based origin estimation

References

• Comin, C. H. and da Fontoura Costa, L. (2011). Identifying the starting point of a spreading process in complex networks. Physical Review E, 84. <DOI: 10.1103/PhysRevE.84.056105>

• Manitz, J., J. Harbering, M. Schmidt, T. Kneib, and A. Schoebel (2016). Source Estimation for Propagation Processes on Complex Networks with an Application to Delays in Public Transportation Systems. Accepted at JRSS-C.

• Manitz, J. (2014). Statistical Inference for Propagation Processes on Complex Networks. Ph.D. thesis, Georg-August-University Goettingen. Verlag Dr.~Hut, ISBN 978-3-8439-1668-4. Available online: http://ediss.uni-goettingen.de/handle/11858/00-1735-0000-0022-5F38-B.

• Manitz, J., Kneib, T., Schlather, M., Helbing, D. and Brockmann, D. (2014). Origin detection during food-borne disease outbreaks - a case study of the 2011 EHEC/HUS outbreak in Germany. PLoS Currents Outbreaks, 1. <DOI: 10.1371/currents.outbreaks.f3fdeb08c5b9de7c09ed9cbcef5f01f2>

See Also

Other origin-est: origin_multiple

Examples

# NOT RUN {
data(delayGoe)

# compute effective distance
data(ptnGoe)
goenet <- igraph::as_adjacency_matrix(ptnGoe, sparse=FALSE)
p <- goenet/rowSums(goenet)
eff <- eff_dist(p)
# apply effective distance median source estimation
om <- origin(events=delayGoe[10,-c(1:2)], type='edm', distance=eff)
summary(om)
plot(om, 'mdist',start=1)
plot(om, 'wvar',start=1)
performance(om, start=1, graph=ptnGoe)

# backtracking origin estimation (Manitz et al., 2016)
ob <- origin(events=delayGoe[10,-c(1:2)], type='backtracking', graph=ptnGoe)
summary(ob)
plot(ob, start=1)
performance(ob, start=1, graph=ptnGoe)

# centrality-based origin estimation (Comin et al., 2011)
oc <- origin(events=delayGoe[10,-c(1:2)], type='centrality', graph=ptnGoe)
summary(oc)
plot(oc, start=1)
performance(oc, start=1, graph=ptnGoe)

# }