high_tech_company — Krackhardt high tech company network
- Description
A multiplex network of relationships among the 21 managers of a small high-tech manufacturing company in the United States, collected by D. Krackhardt with questionnaires as part of his study of cognitive social structures. The version here, distributed by M. De Domenico, has three directed layers:
- advice (190 edges): $(i, j)$ means that manager $i$ reported going to manager $j$ for advice;
- friendship (102 edges): $(i, j)$ means that manager $i$ considers manager $j$ a friend;
- reports to (20 edges): $(i, j)$ means that manager $i$ reports to manager $j$ in the formal hierarchy of the company.
In the original study, each manager was also asked to describe the advice and friendship relations among all other managers; these perceptions are not included here.
The network has the following properties:
- (Node)
nodeAge: age of the manager, in years. - (Node)
nodeTenure: length of service in the company, in years. - (Node)
nodeLevel: level in the corporate hierarchy: 1 = CEO, 2 = vice president, 3 = manager. - (Node)
nodeDepartment: department, coded 1 to 4 (0 for the CEO, who is not in a department). - (Edge)
layer(edges): 1 = advice, 2 = friendship, 3 = reports to; the graph propertylayer_keymaps these numbers to layer names. - (Edge)
weight(edges): always 1.
- Tags
- Social Offline Multilayer Unweighted
- Added
- 2020-08-11
- Citation
-
- D. Krackhardt, "Cognitive social structures". Social Networks (1987), 9, 104-134. 10.1016/0378-8733(87)90009-8 Sci-Hub
- Upstream URL OK
- https://manliodedomenico.com/data.php
- Upstream license
- Open Data Commons Open Database License (ODbL), https://opendatacommons.org/licenses/odbl/
- Networks
-
Tip: hover over a column header for a description.
Column legend
- $\left<k\right>$
- Mean degree
- $\sigma_k$
- Standard deviation of the degrees
- $\lambda_h$
- Largest eigenvalue of the Hashimoto (non-backtracking) matrix
- $\tau$
- Random walk mixing time
- $r$
- Degree assortativity
- $c$
- Global clustering coefficient
- $\oslash$
- (Pseudo-) diameter
- $S$
- Relative size of the largest component
- NPs
- Node properties
- EPs
- Edge properties
- Ridiculograms*
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