freshmen — Zeggelink's freshmen (1999)

Description

Friendship ratings among the 34 freshmen majoring in Sociology at the University of Groningen (Netherlands) in the academic year 1998-1999, collected by E. Zeggelink in collaboration with G. van de Bunt with questionnaires. Except for a few prior acquaintances, the students did not know each other at the start. Each student rated their relationship with every other student on a scale from best friendship to troubled relationship. The data were collected at seven time points, of which five are available: t0, at the start of the year (during an introduction period), and t2, t3, t5 and t6, about 6, 9, 21 and 27 weeks later. There is increasing nonresponse at the later time points.

Each network is a complete directed graph with self-loops, in which the edge $(i, j)$ stores the rating given by student $i$ to student $j$; therefore, edges should be selected according to their weight values.

The networks have the following properties (the vertex attributes were collected at t0):

(Node) id: the student's ID.
(Node) gender: 1 = male, 2 = female.
(Node) age: age in years (99 = missing).
(Node) program: 1 = regular four-year program, 2 = two-year program (for students who previously completed higher vocational training; these are typically older).
(Node) smoking: 1 = no, 2 = yes, at parties, 3 = 1-3 cigarettes per day, 4 = 4-10 per day, 5 = more than 10 per day, 99 = missing.
(Edge) weight (edges): the rating of $j$ by $i$: 1 = best friendship, 2 = friendship, 3 = friendly relationship, 4 = neutral relationship, 5 = troubled relationship, 6 = item non-response (according to the data providers, also often used to express "unknown person", for which the code 0 is otherwise reserved), 8 = diagonal (after t0), 9 = actor non-response.

Tags

Social Offline Weighted Temporal Metadata

Added

2020-08-11

Citation

G. Van de Bunt, M. Van Duijn, and T. Snijders. "Friendship networks through time: An actor-oriented dynamic statistical network model." Computational & Mathematical Organization Theory 5.2. 167-192. (1999). 10.1023/A:1009683123448 Sci-Hub

Upstream URL OK

http://www.stats.ox.ac.uk/~snijders/siena/Zeggelink_data.htm

Networks

Tip: click on a column header to sort the list, or hover over it 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

Name	Nodes	Edges	$\left<k\right>$	$\lambda_h$	$\tau$	$r$	$c$	$\oslash$	$S$	Kind	Mode	NPs	EPs	gt	GraphML	GML	csv
t0	34	1,156	34.00	34.00	0.03	nan	0.98	1	1.00	Directed	Unipartite	id gender age program smoking	weight	1 KiB	5 KiB	4 KiB	3 KiB
t2	34	1,156	34.00	34.00	0.03	nan	0.98	1	1.00	Directed	Unipartite	id gender age program smoking	weight	1 KiB	5 KiB	4 KiB	4 KiB
t3	34	1,156	34.00	34.00	0.03	nan	0.98	1	1.00	Directed	Unipartite	id gender age program smoking	weight	1 KiB	5 KiB	4 KiB	4 KiB
t5	34	1,156	34.00	34.00	0.03	nan	0.98	1	1.00	Directed	Unipartite	id gender age program smoking	weight	1 KiB	5 KiB	4 KiB	4 KiB
t6	34	1,156	34.00	34.00	0.03	nan	0.98	1	1.00	Directed	Unipartite	id gender age program smoking	weight	1 KiB	5 KiB	4 KiB	4 KiB

Ridiculograms^*

^* These are automatically generated force-directed visualizations, and can be quite meaningless for networks both big and small. They should not be taken seriously as sources of scientific insight. See here for a discussion.