# Email Based Institutional Network Analysis: Applications and Risks

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## Abstract

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## 1. Introduction

- Suitable indicators that take into account the direction, intensity and frequency of information flow among the organisation’s members.
- How such indicators can be connected with human resources and the overall organisation efficiency measurement.
- What are the possible areas of application and the trade-offs when applying such an approach?

## 2. Research on Emails and Organizational Dynamics

## 3. Description of the Dataset

## 4. Modelling Based on Social Network Analysis Indicators

- ${\tau}_{i}$ is the number of triangles formed between node i and its possible neighbours.
- ${d}_{i}$ is the degree of the node (the number of individual connections).

- Middleperson: a triangle where the two arcs of i have different directions and there is an arc between j and k (or vice versa), without forming a cycle. There are two arcs incoming to k or j (j→i, i→k, j→k or vice versa) (Figure 2d)

^{2}is remarkably high at 0.8781. The number of emails sent from i to j appears to be positively correlated with the out-closeness centrality of i—i.e., how close i is to the centre of the network as regards sending emails. Nevertheless, it is negatively correlated with the in-closeness centrality of i. Seen from the j point of view, the in- and out-closeness centrality estimates have—as expected—the opposite signs. The correlation with the local clustering coefficient is not as straightforward. The in-, out- and middleperson indicators of i show a positive correlation, while the cycle clustering coefficient has a negative correlation. On the side of j, the correlations are not symmetrical. It may be implied that the middleperson role generates more email activity, while the cycle role generates less, for both i and j. It is also interesting to note that the strength of email exchanges is expected to be higher when i and j belong to the same department.

^{2}is lower (0.5556), but still acceptable. Most probably, the influence of possible weekends between the original email and its reply distorts the results. Even so, the estimates for the independent variable are in the expected direction. Individuals with high out-closeness centrality are expected to reply (as j) and be replied to (as i) faster than the average, while high in-closeness has the opposite effect. The in- and out-clustering coefficients have opposite signs, but have the same direction for i and j. This suggests that individuals with a high out-clustering indicator reply and are replied to faster. High in-clustering or cycling clustering coefficients suggest higher delays in replies. The middleperson clustering coefficient has a negative time for i and a positive one for j. Finally, similarly to the case of intensity, the time to reply is, on average, lower when i and j belong to the same department.

## 5. Discussion

## 6. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## Disclaimer

## References

- AbouAssi, Khaldoun, and Mary Tschirhart. 2018. Organizational Response to Changing Demands: Predicting Behavior in Donor Networks. Public Administration Review 78: 126–36. [Google Scholar] [CrossRef]
- Avrahami, Daniel, Susan R. Fussell, and Scott E. Hudson. 2008. IM waiting: Timing and responsiveness in semi-synchronous communication. Paper presented at the ACM Conference on Computer Supported Cooperative Work, CSCW, San Diego, CA, USA, November 8–12; pp. 285–94. [Google Scholar]
- Biswas, Anupam, and Bhaskar Biswas. 2015. Investigating community structure in perspective of ego network. Expert Systems with Applications 42: 6913–34. [Google Scholar] [CrossRef]
- Christidis, Panayotis. 2019. Social Network Analysis of e-mail traffic using directed and weighted graphs. Symmetry. forthcoming. [Google Scholar]
- Christidis, Panayotis, and Caralampo Focas. 2019. Factors affecting the uptake of hybrid and electric vehicles in the European union. Energies 12: 3414. [Google Scholar] [CrossRef]
- Clemente, Gian Paolo, and Rosanna Grassi. 2018. Directed clustering in weighted networks: a new perspective. Chaos, Solitons and Fractals 107: 26–38. [Google Scholar] [CrossRef]
- Csardi, Gabor, and Tamas Nepusz. 2006. The igraph software package for complex network research. InterJournal, Complex Systems 1695: 1–9. [Google Scholar]
- Domingos, Pedro, and Matt Richardson. 2001. Mining the network value of customers. Paper presented at the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, August 26–29; pp. 57–66. [Google Scholar]
- Falzon, Lucia, Eric Quintane, John Dunn, and Garry Robins. 2018. Embedding time in positions: Temporal measures of centrality for social network analysis. Social Networks 54: 168–78. [Google Scholar] [CrossRef]
- Focas, Caralampo, and Panayotis Christidis. 2017. Peak Car in Europe? Transportation Research Procedia 25: 531–50. [Google Scholar] [CrossRef]
- Freeman, L. C. 1979. Centrality in networks: I. Conceptual clarification. Social Networks 1: 215–39. [Google Scholar] [CrossRef]
- Gloor, Peter A., and Andrea Fronzetti Colladon. 2015. Measuring organizational consciousness through e-Mail based social network analysis. Paper presented at the 5th International Conference on Collaborative Innovation Networks COINs15, Tokyo, Japan, March 12–14. [Google Scholar]
- Gloor, Peter A., Adam Almozlino, Orr Inbar, Wei Lo, and Shannon Provost. 2014. Measuring team creativity through longitudinal social signals. arXiv arXiv:1407.0440. [Google Scholar]
- Holme, Petter. 2015. Modern temporal network theory: a colloquium. The European Physical Journal B 88: 234. [Google Scholar] [CrossRef][Green Version]
- Holme, Petter, and Jari Saramäki. 2012. Temporal networks. Physic Reports 519: 97–125. [Google Scholar] [CrossRef][Green Version]
- Kalman, Yoram M., Gilad Ravid, Daphne R. Raban, and Sheizaf Rafaeli. 2006. Pauses and Response Latencies: A Chronemic Analysis of Asynchronous CMC. Journal of Computer-Mediated Communication 12: 1–23. [Google Scholar] [CrossRef][Green Version]
- Kalman, Yoram M., Lauren E. Scissors, Alastair J. Gill, and Darren Gergle. 2013. Online chronemics convey social information. Computers in Human Behavior 29: 1260–69. [Google Scholar] [CrossRef]
- Kolli, Naimisha, and Balakrishnan Narayanaswamy. 2013. Analysis of e-mail communication using a social network framework for crisis detection in an organization. Procedia—Social and Behavioral Sciences 100: 57–67. [Google Scholar] [CrossRef]
- Lee, Kyu-Min, Byungjoon Min, and Kwang-Il Goh. 2015. Towards real-world complexity: an introduction to multiplex networks. The European Physical Journal B 88: 48. [Google Scholar] [CrossRef]
- Leskovec, Jure, Jon Kleinberg, and Christos Faloutsos. 2007. Faloutsos. Graph Evolution: Densification and Shrinking Diameters. ACM Transactions on Knowledge Discovery from Data (ACM TKDD) 1: 2. [Google Scholar] [CrossRef]
- Li, Ze, Duoyong Sun, Renqi Zhu, and Zihan Lin. 2017. Detecting event-related changes in organizational networks using optimized neural network models. PLoS ONE 12: 1–21. [Google Scholar] [CrossRef]
- Losada, Marcial. 1999. The complex dynamic of high performance teams. Mathematical and Computer Modelling 30: 179–92. [Google Scholar] [CrossRef]
- Lou, Tiancheng, Jie Tang, John Hopcroft, Zhanpeng Fang, and Xiaowen Ding. 2013. Learning to predict reciprocity and triadic closure in social networks. ACM Transactions on Knowledge Discovery from Data 7: 2499908. [Google Scholar] [CrossRef]
- Melhorado, Ana Margarida Condeço, Hande Demirel, Mert Kompil, Elena Navajas, and Panayotis Christidis. 2016. The impact of measuring internal travel distances on self-potentials and accessibility. European Journal of Transport and Infrastructure Research 16: 300–18. [Google Scholar]
- Merten, Frank, and Peter Gloor. 2010. Too Much E-Mail Decreases Job Satisfaction. Procedia Social and Behavioral Sciences 2: 6457–65. [Google Scholar] [CrossRef]
- Michail, Othon. 2015. An introduction to temporal graphs: An algorithmic perspective. arXiv arXiv:1503.00278. [Google Scholar]
- Michalski, Radosław, Sebastian Palus, and Przemysław Kazienko. 2011. Matching Organizational Structure and Social Network Extracted from Email Communication. Paper presented at the 5th International Conference on Collaborative Innovation Networks COINs15, Tokyo, Japan, March 12–14. [Google Scholar]
- Nawaz, Waqas, Kifayat-Ullah Khan, and Young-Koo Lee. 2016. A multi-user perspective for personalized email communities. Expert Systems with Applications 54: 265–83. [Google Scholar] [CrossRef]
- Polidoro, Francisco, Jr., Gautam Ahuja, and Will Mitchell. 2011. When the Social Structure Overshadows Competitive Incentives: The Effects of Network Embeddedness on Joint Venture Dissolution. Academy of Management Journal 54: 203–23. [Google Scholar] [CrossRef]
- Scholtes, Ingo, Nicolas Wider, René Pfitzner, Antonios Garas, Claudio J. Tessone, and Frank Schweitzer. 2014. Causality-driven slow-down and speed-up of diffusion in non-Markovian temporal networks. Nature Communications 5: 5024. [Google Scholar] [CrossRef][Green Version]
- Wang, Zhongqing, L. I. Shoushan, Fang Kong, and Guodong Zhou. 2013. Collective personal profile summarization with social networks. Paper presented at the EMNLP 2013—2013 Conference on Empirical Methods in Natural Language Processing, Seattle, WA, USA, October 18–21; pp. 715–25. [Google Scholar]
- Watts, Duncan J., and Steven H. Strogatz. 1998. Collective dynamics of ‘small-world’ networks. Nature 393: 440–42. [Google Scholar] [CrossRef]
- Webb, Eugene T., Donald T. Campbell, Richard D. Schwartz, Lee Sechrest, and Janet Belew Grove. 1966. Unobtrusive Measures: Nonreactive Research in the Social Sciences. Oxford: Rand Mcnally. [Google Scholar]
- Yin, Hao, Austin R. Benson, Jure Leskovec, and David F. Gleich. 2017. Local Higher-order Graph Clustering. Paper presented at the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13–17. [Google Scholar]
- Zenk, Lukas, Christoph Stadtfeld, and Florian Windhager. 2010. How to analyze dynamic network patterns of high performing teams. Procedia Social and Behavioral Sciences 2: 6418–22. [Google Scholar] [CrossRef]
- Zhuang, Honglei, Jie Tang, Wenbin Tang, Tiancheng Lou, Alvin Chin, and Xia Wang. 2012. Actively learning to infer social ties. Data Mining and Knowledge Discovery 25: 270–97. [Google Scholar] [CrossRef]

**Figure 1.**Total number of emails sent and received by each department (bubble size equals number of department members).

Department | Members | Sent | Received | Sent Per Member | Received Per Member |
---|---|---|---|---|---|

D_4 | 109 | 38,614 | 39,693 | 354 | 364 |

D_14 | 92 | 31,747 | 34,298 | 345 | 373 |

D_1 | 65 | 27,829 | 23,234 | 428 | 357 |

D_21 | 61 | 23,195 | 21,528 | 380 | 353 |

D_22 | 25 | 18,906 | 7501 | 756 | 300 |

D_15 | 55 | 18,437 | 17,905 | 335 | 326 |

D_0 | 49 | 17,075 | 18,477 | 348 | 377 |

D_7 | 51 | 15,108 | 17,018 | 296 | 334 |

D_17 | 35 | 13,077 | 11,995 | 374 | 343 |

D_13 | 26 | 12,800 | 10,464 | 492 | 402 |

id | Sent | Received | Department |
---|---|---|---|

987 | 9782 | 789 | D_22 |

629 | 6585 | 2843 | D_1 |

178 | 5939 | 1684 | D_0 |

168 | 5664 | 4710 | D_4 |

586 | 5106 | 2810 | D_21 |

356 | 4905 | 2723 | D_8 |

98 | 4531 | 1416 | D_16 |

746 | 4153 | 1472 | D_15 |

288 | 3806 | 484 | D_37 |

316 | 3768 | 1722 | D_1 |

id | Sent | Received | Department |
---|---|---|---|

168 | 5664 | 4710 | D_4 |

912 | 3590 | 3223 | D_14 |

947 | 2243 | 3042 | D_7 |

629 | 6585 | 2843 | D_1 |

586 | 5106 | 2810 | D_21 |

356 | 4905 | 2723 | D_8 |

472 | 1550 | 2717 | D_6 |

915 | 3705 | 2513 | D_11 |

891 | 2069 | 2305 | D_38 |

416 | 2100 | 2302 | D_9 |

Intensity of Bilateral Emails $\mathbf{log}({\mathit{w}}_{\mathit{i}\mathit{j}})$ | Time for Email to Be Replied to $\mathbf{log}({\mathit{t}}_{\mathit{j}\mathit{i}})$ | |||||
---|---|---|---|---|---|---|

Independent Variable | Estimate | Level of Significance Pr(>|t|) | Estimate | Level of Significance Pr(>|t|) | ||

${C}_{i}^{\text{}closeness,in}$ | −4.70199 | <2 × 10^{16} | *** | 6.803725 | <2 × 10^{16} | *** |

${C}_{i}^{\text{}closeness,out}$ | 8.127924 | <2 × 10^{16} | *** | −6.31347 | <2 × 10^{16} | *** |

${C}_{i}^{in}$ | 0.572814 | <2 × 10^{16} | *** | 1.258198 | <2 × 10^{16} | *** |

${C}_{i}^{out}$ | 0.434099 | <2 × 10^{16} | *** | −0.55758 | <2 × 10^{16} | *** |

${C}_{i}^{middleperson}$ | 1.225002 | <2 × 10^{16} | *** | −2.5339 | <2 × 10^{16} | *** |

${C}_{i}^{cycle}$ | −2.37641 | <2 × 10^{16} | *** | 1.35217 | <2 × 10^{16} | *** |

${C}_{j}^{\text{}closeness,in}$ | 7.661745 | <2 × 10^{16} | *** | 15.34833 | <2 × 10^{16} | *** |

${C}_{j}^{\text{}closeness,out}$ | −9.46001 | <2 × 10^{16} | *** | −10.6287 | <2 × 10^{16} | *** |

${C}_{j}^{in}$ | −1.55559 | <2 × 10^{16} | *** | 0.38119 | <2 × 10^{16} | *** |

${C}_{j}^{out}$ | −0.83673 | <2 × 10^{16} | *** | −0.86216 | <2 × 10^{16} | *** |

${C}_{j}^{middleperson}$ | 3.620311 | <2 × 10^{16} | *** | 0.375042 | 1.63 × 10^{11} | *** |

${C}_{j}^{cycle}$ | −1.15051 | <2 × 10^{16} | *** | 0.193783 | 0.00147 | ** |

$Dep{t}_{i}=Dep{t}_{j}$ | 0.051719 | <2 × 10^{16} | *** | −0.18322 | <2 × 10^{16} | *** |

Adjusted R-squared | 0.8781 | 0.5556 | ||||

p-value | <2.2 × 10^{16} | <2.2 × 10^{16} |

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Christidis, P.; Gomez Losada, Á. Email Based Institutional Network Analysis: Applications and Risks. *Soc. Sci.* **2019**, *8*, 306.
https://doi.org/10.3390/socsci8110306

**AMA Style**

Christidis P, Gomez Losada Á. Email Based Institutional Network Analysis: Applications and Risks. *Social Sciences*. 2019; 8(11):306.
https://doi.org/10.3390/socsci8110306

**Chicago/Turabian Style**

Christidis, Panayotis, and Álvaro Gomez Losada. 2019. "Email Based Institutional Network Analysis: Applications and Risks" *Social Sciences* 8, no. 11: 306.
https://doi.org/10.3390/socsci8110306