sp_hypertext — Hypertext 2009 dynamic contact network

Description

The temporal network of contacts among attendees of the ACM Hypertext 2009 conference, which spanned 2.5 days of time.¹

This dataset was collected during the ACM Hypertext 2009 conference, where the SocioPatterns project deployed the Live Social Semantics application. Conference attendees volunteered to wear radio badges that monitored their face-to-face proximity. The dataset published here represents the dynamical network of face-to-face proximity of ~110 conference attendees over about 2.5 days. No personal data are released here, and no metadata collected by the Live Social Semantics application are exposed. We provide two data files, described below.

Contact List. This is a tab-separated list representing the active contacts during 20-second intervals of the data collection. Each line has the form “t i j“, where i and j are the anonymous IDs of the persons in contact, and the interval during which this contact was active is [ t – 20s, t ]. If multiple contacts are active in a given interval, you will see multiple lines starting with the same value of t. Time is measured in seconds since 8am on Jun 29th 2009 (UNIX ctime 1246255200).

Contact Intervals. This file is in JSON format and contains a dictionary. Each key is a person ID and the corresponding value is a dictionary of neighbors of that person in the contact network. This dictionary of neighbors has person IDs as keys and, for each key, the value gives the list of time intervals during which the corresponding contact was active. Time is measured as above.

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1. Description obtained from the ICON project. ↩

Tags

Social Offline Unweighted Temporal

Citation

• L. Isella et al., "What’s in a crowd? Analysis of face-to-face behavioral networks." J. Theor. Bio. 271, 166-180 (2011)., http://arxiv.org/abs/1006.1260

Upstream URL OK

http://www.sociopatterns.org/datasets/hypertext-2009-dynamic-contact-network/

Networks

Tip: click on the table header to sort the list. Hover your mouse over it to obtain a legend.

Name	Nodes	Edges	$\left<k\right>$	$\sigma_k$	$\lambda_h$	$\tau$	$r$	$c$	$\oslash$	$S$	Kind	Mode	NPs	EPs	gt	GraphML	GML	csv
contacts	113	20,818	368.46	322.99	45.63	13.09	0.32	0.55	3	1.00	Undirected	Unipartite	id	time	38 KiB	81 KiB	75 KiB	60 KiB
intervals	113	2,196	38.87	18.35	45.63	0.81	-0.12	0.50	3	1.00	Undirected	Unipartite	id	intervals	51 KiB	74 KiB	72 KiB	54 KiB

Ridiculograms