Abstract
We describe the multi-layer temporal network which connects a population of more than 700 university students over a period of four weeks. The dataset was collected via smartphones as part of the Copenhagen Networks Study. We include the network of physical proximity among the participants (estimated via Bluetooth signal strength), the network of phone calls (start time, duration, no content), the network of text messages (time of message, no content), and information about Facebook friendships. Thus, we provide multiple types of communication networks expressed in a single, large population with high temporal resolution, and over a period of multiple weeks, a fact which makes the dataset shared here unique. We expect that reuse of this dataset will allow researchers to make progress on the analysis and modeling of human social networks.
Highlights
Background & SummaryThe purpose of collecting the Copenhagen Networks Study (CNS) dataset was to accelerate our understanding of social systems
At this high temporal resolution, the network consists of many small connected components which can be directly used for network analysis[36]
Using a consistent user id in all the data types, allows for straightforward merging of different subsets, allowing us to, for example, consider dynamics of communication separately for different genders
Summary
The purpose of collecting the Copenhagen Networks Study (CNS) dataset was to accelerate our understanding of social systems. Geo-located tweets or Instagram posts, social physical activity app data, etc can be used to re-identify geospatial data To avoid this attack, we limit our release to information that cannot be cross-correlated with public datasets. There is a number of publications covering technical aspects of data collection and analysis[16,32,33,34,35] Another set of papers focused on modeling and analyzing network structure[36,37,38,39,40,41], epidemiology[42,43,44], as well as work on human mobility[45,46,47,48,49,50], and privacy[14,46]. Given that we are only able to release network data, we expect reuse of this dataset to focus on the modeling and analysis of multi-layer temporal networks and we hope that the data released here will allow researchers to make progress on understanding human social networks
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