117
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Measuring Large-Scale Social Networks with High Resolution

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          This paper describes the deployment of a large-scale study designed to measure human interactions across a variety of communication channels, with high temporal resolution and spanning multiple years—the Copenhagen Networks Study. Specifically, we collect data on face-to-face interactions, telecommunication, social networks, location, and background information (personality, demographics, health, politics) for a densely connected population of 1 000 individuals, using state-of-the-art smartphones as social sensors. Here we provide an overview of the related work and describe the motivation and research agenda driving the study. Additionally, the paper details the data-types measured, and the technical infrastructure in terms of both backend and phone software, as well as an outline of the deployment procedures. We document the participant privacy procedures and their underlying principles. The paper is concluded with early results from data analysis, illustrating the importance of multi-channel high-resolution approach to data collection.

          Related collections

          Most cited references103

          • Record: found
          • Abstract: found
          • Article: not found
          Is Open Access

          Community detection in graphs

          The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Such clusters, or communities, can be considered as fairly independent compartments of a graph, playing a similar role like, e. g., the tissues or the organs in the human body. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. This problem is very hard and not yet satisfactorily solved, despite the huge effort of a large interdisciplinary community of scientists working on it over the past few years. We will attempt a thorough exposition of the topic, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY

              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Understanding individual human mobility patterns

              Despite their importance for urban planning, traffic forecasting, and the spread of biological and mobile viruses, our understanding of the basic laws governing human motion remains limited thanks to the lack of tools to monitor the time resolved location of individuals. Here we study the trajectory of 100,000 anonymized mobile phone users whose position is tracked for a six month period. We find that in contrast with the random trajectories predicted by the prevailing Levy flight and random walk models, human trajectories show a high degree of temporal and spatial regularity, each individual being characterized by a time independent characteristic length scale and a significant probability to return to a few highly frequented locations. After correcting for differences in travel distances and the inherent anisotropy of each trajectory, the individual travel patterns collapse into a single spatial probability distribution, indicating that despite the diversity of their travel history, humans follow simple reproducible patterns. This inherent similarity in travel patterns could impact all phenomena driven by human mobility, from epidemic prevention to emergency response, urban planning and agent based modeling.
                Bookmark

                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2014
                25 April 2014
                : 9
                : 4
                : e95978
                Affiliations
                [1 ]DTU Compute, Technical University of Denmark, Kgs. Lyngby, Denmark
                [2 ]The Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark
                [3 ]Department of Anthropology, University of Copenhagen, Copenhagen, Denmark
                University of Zaragoza, Spain
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: AS VS PS AC MMM JEL SL. Performed the experiments: AS VS PS AC MMM JEL SL. Analyzed the data: AS VS PS AC SL. Wrote the paper: AS VS PS AC MMM JEL SL.

                Article
                PONE-D-14-06887
                10.1371/journal.pone.0095978
                4000208
                24770359
                494a316a-c732-48e7-b406-da5118a52f3e
                Copyright @ 2014

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 15 February 2014
                : 2 April 2014
                Page count
                Pages: 24
                Funding
                The SensibleDTU project was made possible by a Young Investigator Grant from the Villum Foundation (High Resolution Networks, awarded to SL). Scaling the project up to 1 000 individuals in 2013 was made possible by a interdisciplinary UCPH 2016 grant, Social Fabric (PI David Dreyer Lassen, SL is co-PI) focusing mainly on the social and basic science elements of the project. This grant has funded purchase of the smartphones, as well as technical personnel. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Computer and Information Sciences
                Network Analysis
                Social Networks
                Software Engineering
                Physical Sciences
                Physics
                Interdisciplinary Physics
                Social Sciences
                Sociology
                Computational Sociology
                Social Systems

                Uncategorized
                Uncategorized

                Comments

                Comment on this article