README

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`NetOrigin` package

GitHub repo size GitHub issues

CRAN License CRAN dependencies status Website

Performs network-based source estimation. Different approaches are available: effective distance median, recursive backtracking, and centrality-based source estimation. Additionally, we provide public transportation network data as well as methods for data preparation, source estimation performance analysis and visualization.

Installation

You can install the latest production version from CRAN

install.packages("NetOrigin", dependencies = TRUE)

or the current development version from GitHub

library("devtools")
install_github("jmanitz/NetOrigin")

Then, load the package

library("NetOrigin")

Example: Effective Distance

data(delayGoe)

# compute effective distance
data(ptnGoe)
goenet <- igraph::as_adjacency_matrix(ptnGoe, sparse=FALSE)
p <- goenet/rowSums(goenet)
eff <- eff_dist(p)

## Computing the effective distance between 257 nodes:
##  1...................................................................................................
##  100...................................................................................................
##  200.........................................................done

# apply effective distance median source estimation
om <- origin(events=delayGoe[10,-c(1:2)], type='edm', distance=eff)
summary(om)

## Effective distance median origin estimation:
## 
## estimated node of origin 91: X.Gotthelf.Leimbach.Strasse 
## 
## auxiliary variables:
##        id          events            wmean             wvar       
##  Min.   :  1   Min.   : 0.0000   Min.   : 5.482   Min.   :0.3987  
##  1st Qu.: 65   1st Qu.: 0.0000   1st Qu.:21.572   1st Qu.:2.2761  
##  Median :129   Median : 0.0000   Median :27.345   Median :2.4050  
##  Mean   :129   Mean   : 0.6459   Mean   :26.948   Mean   :2.4989  
##  3rd Qu.:193   3rd Qu.: 0.0000   3rd Qu.:33.359   3rd Qu.:2.9986  
##  Max.   :257   Max.   :46.0000   Max.   :47.762   Max.   :6.2052  
##      mdist      
##  Min.   :14.34  
##  1st Qu.:20.75  
##  Median :24.23  
##  Mean   :24.92  
##  3rd Qu.:28.88  
##  Max.   :39.16

plot(om, 'mdist', start=1)

plot(om, 'wvar', start=1)

performance(om, start=1, graph=ptnGoe)

##                   start                         est  hitt rank spj dist
## 1 X.Adolf.Hoyer.Strasse X.Gotthelf.Leimbach.Strasse FALSE    2   2 1332

Example: Backtracking

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

## Backtracking origin estimation:
## 
## estimated node of origin 87: X.Gesundbrunnen 
## 
## auxiliary variables:
##        id          events            bcount       
##  Min.   :  1   Min.   : 0.0000   Min.   :0.00000  
##  1st Qu.: 65   1st Qu.: 0.0000   1st Qu.:0.00000  
##  Median :129   Median : 0.0000   Median :0.00000  
##  Mean   :129   Mean   : 0.6459   Mean   :0.03891  
##  3rd Qu.:193   3rd Qu.: 0.0000   3rd Qu.:0.00000  
##  Max.   :257   Max.   :46.0000   Max.   :3.00000

plot(ob, start=1)

performance(ob, start=1, graph=ptnGoe)

##                   start             est  hitt rank spj dist
## 1 X.Adolf.Hoyer.Strasse X.Gesundbrunnen FALSE    4   8 5328

Example: Multiple Origins

data(ptnAth)
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

origin_multiple(events=delayAth[10,-c(1:2)], type='edm', graph=ptnAth, no=2, distance=eff)

## [[1]]
## Effective distance median origin estimation:
## 
## estimated node of origin 3: 3 
## 
## [[2]]
## Effective distance median origin estimation:
## 
## estimated node of origin 2: 2

References

Li, J., J. Manitz, E. Bertuzzo, and E.D. Kolaczyk (2021): Sensor-based localization of epidemic sources on human mobility networks. PLoS Comput Biol 17(1): e1008545. <DOI: 10.1371/journal.pcbi.1008545>
Manitz, J., J. Harbering, M. Schmidt, T. Kneib, and A. Schoebel (2017): Source Estimation for Propagation Processes on Complex Networks with an Application to Delays in Public Transportation Systems. Journal of Royal Statistical Society C (Applied Statistics), 66: 521–536. <DOI: 10.1111/rssc.12176>
Manitz, J., T. Kneib, M. Schlather, J. Helbing, and D. Brockmann (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>
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>

To cite package ‘NetOrigin’ in publications use:

Juliane Manitz (2018). NetOrigin: Origin Estimation for Propagation Processes on Complex Networks. R package version 1.0-3. https://CRAN.R-project.org/package=NetOrigin

Use toBibtex(citation("NetOrigin")) in R to extract BibTeX references.