Abstract
Creating a social network dataset requires us to represent a set of empirical observations according to a specific conceptual understanding. This requires a number of design decisions for the conceptual framework, which is then implemented through a data structure. In this paper, we propose a standard schema to describe these decisions. A standard schema allows us to define the conceptual framework, structure, and content of a dataset. Social network datasets may contain many features. Beyond the definition of actors and relations, network data may include: actor or relation attributes; data for multiple observation periods (dynamic data); or parallel event data. The creation of a network dataset may also involve the application of specific boundary conditions, sampling approaches or may include missing data. Our proposed schema is designed to support a scientific approach to social network analysis by making these features and assumptions transparent and easy to communicate. We believe that this will facilitate researchers through the design, creation, communication, and evaluation of social network datasets. To develop this schema, we gathered and analysed the structure, content, and metadata of over 150 publicly available social network datasets drawing from multiple disciplines, including statistics, computer science, sociology, economics, and political science.
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Notes
A tangible example of this is the myriad of different social network data storage file formats. There currently exist at least 20 standard social network storage formats (including gexf, gml, GraphML, UNINet DL, Pajek NET, csv, edgelist, etc.) and many non-standard storage approaches. This can make data difficult to access for those unfamiliar with the data format and hence reduce the discovery of these datasets by specific disciplines.
Here, we use the term feature according to its standard interpretation: ‘a distinctive attribute or aspect of something’.
University College Dublin Dynamics Lab http://dl.ucd.ie.
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This research is funded under Irish Government PRTLI Cycle 5 Simulation Sciences Programme and is co-funded under the European Regional Development Fund of the European Union.
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O’Loughlin, E., Payne, D. Building rich social network data: a schema to assist in designing, collecting and evaluating social network data. Soc. Netw. Anal. Min. 4, 198 (2014). https://doi.org/10.1007/s13278-014-0198-0
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DOI: https://doi.org/10.1007/s13278-014-0198-0