networkDynamicData (version 0.2.1)

manufacturingEmails: Internal Emails from a Polish Manufacturing Company

Description

The source is a longitudinal network describing the history of internal e-mail communication (sender, recipient, datetime) between 167 employees of a mid-sized manufacturing company located in Poland. Multiple recipients of the same e-mail (To, CC, BCC) are represented as separate rows without distinguishing the recipient type. The period covered are nine full months of 2010 starting from 2010-01-01 to 2010-09-30 (event dates in local time). Apart from the communication, information about who in the company reports to whom is included . Node #86 is the CEO (the only loop in the graph).

Usage

data("manufacturingEmails")

Format

a networkDynamic object

Source

radoslaw.michalski pwr.edu.pl http://www.ii.pwr.wroc.pl/~michalski/index.php?content=datasets#manufacturing and

License

The data are distributed to the public under a Creative Commons Attribution-NonCommercial-ShareAlike license http://creativecommons.org/licenses/by-nc-sa/3.0/.

Details

This dataset consists of two network objects:

The manufacturingEmails network is a networkDynamic object with 82614 edge spells (emails communications) between 176 employees. The network is represented as a continuous time event temporal model (onset=terminus). Edge timing is coded as numeric POSIX time (seconds) with event dates in local time ranging from 1262482810 (2010-01-01) until 1285909692 (2010-09-30). The network contains self-loops. Duplicate rows in the input data (email to the same recipient at the same second using TO, CC, BCC etc) have been collapsed but this information is preserved in the numEmailTypes dynamic edge attribute. The networks included here have a much larger vertex set and so do not correspond exactly to the description in the paper (below).

The manufacturingReportsTo network a static network object which includes the organizational hierarchy. Note that vertices 4, 10, 21, 23, 24, 26 and 46 are technical email accounts not used by employees, and vertices 51, 75, 87, 93, 111 and 139 are email accounts corresponding to former employees and so appear as isolates in the manufacturingReportsTo network.

Description from paper:

... company is a manufacturing company located in Poland. The company employs 300 persons, whereas 1/3 are clerical workers, the rest - laborers. The period analyzed was half a year. The type of organizational structure is functional [3]. However, due to organization operating model and its consequences to organizational structure clarity as well as logs interpretation possibility, only a subset of organization have been chosen for current analysis: 49 clerical employees not directly related to manufacturing process. Three-level management structure exists in the selected company part: management board (2 persons), managers (11 persons) and regular employees (36 persons) and they work in twelve different departments. There were no organizational changes during the analyzed period. Email logs were source data used to build social network . Because of email logs structure, there was no distinction between To, CC and BCC recipients. The resulting set of data contained 11,816 emails in total.

References

When using this dataset, please cite:

Michalski, R., Kajdanowicz, T., Brodka, P., Kazienko, P.: Seed Selection for Spread of Influence in Social Networks: Temporal vs. Static Approach. New Generation Computing (JCR-listed journal), Vol. 32, Issue 3-4, pp. 213-235. Ohmsha-Japan and Springer (2014))

@article{michalski2014seed,
  title={Seed Selection for Spread of Influence in Social Networks: Temporal vs. Static Approach},
  author={Michalski, Rados{\l}aw and Kajdanowicz, Tomasz and Br{\'o}dka, Piotr and Kazienko, Przemys{\l}aw},
  journal={New Generation Computing},
  volume={32},
  number={3-4},
  pages={213--235},
  year={2014},
  publisher={Springer}
}

Michalski, R., Palus, S., Kazienko, P.: Matching Organizational Structure and Social Network Extracted from Email Communication. Lecture Notes in Business Information Processing LNBIP, vol. 87, pp. 197-206, Springer, Berlin Heidelberg (2011)

Examples

data(manufacturingEmails)
## Not run: 
# # plot the organizational hierarchy
# plot(manufacturingReportsTo,displaylabels=TRUE,
#      vertex.cex=0.6,label.cex=0.6,edge.col='gray')
#      
# # plot the first two days of emails
# plot(network.extract(manufacturingEmails,
#      onset=1262482810,length=60*60*24*2))
#      
# # plot email density over time
# plot(density(as.data.frame(manufacturingEmails)$onset))
# 
# # convert date string to POSIX seconds
# as.numeric(as.POSIXct('2010-09-30',format='%Y-%m-%d'))
# 
# # convert POSIX seconds to date string
# as.POSIXct(1285830000,origin='1970-01-01',tz = 'PL')
# ## End(Not run)