Multidimensional Human Dynamics in Mobile Phone Communications
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{"title"=>"Multidimensional human dynamics in mobile phone communications", "type"=>"journal", "authors"=>[{"first_name"=>"Christian", "last_name"=>"Quadri", "scopus_author_id"=>"54929336800"}, {"first_name"=>"Matteo", "last_name"=>"Zignani", "scopus_author_id"=>"36811000900"}, {"first_name"=>"Lorenzo", "last_name"=>"Capra", "scopus_author_id"=>"7003753913"}, {"first_name"=>"Sabrina", "last_name"=>"Gaito", "scopus_author_id"=>"9636090900"}, {"first_name"=>"Gian Paolo", "last_name"=>"Rossi", "scopus_author_id"=>"35194784700"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"373631647", "issn"=>"19326203", "doi"=>"10.1371/journal.pone.0103183", "scopus"=>"2-s2.0-84904864043", "pmid"=>"25068479", "sgr"=>"84904864043"}, "id"=>"73d89477-a0ff-3a52-a46c-f2ff2284c511", "abstract"=>"In today's technology-assisted society, social interactions may be expressed through a variety of techno-communication channels, including online social networks, email and mobile phones (calls, text messages). Consequently, a clear grasp of human behavior through the diverse communication media is considered a key factor in understanding the formation of the today's information society. So far, all previous research on user communication behavior has focused on a sole communication activity. In this paper we move forward another step on this research path by performing a multidimensional study of human sociality as an expression of the use of mobile phones. The paper focuses on user temporal communication behavior in the interplay between the two complementary communication media, text messages and phone calls, that represent the bi-dimensional scenario of analysis. Our study provides a theoretical framework for analyzing multidimensional bursts as the most general burst category, that includes one-dimensional bursts as the simplest case, and offers empirical evidence of their nature by following the combined phone call/text message communication patterns of approximately one million people over three-month period. This quantitative approach enables the design of a generative model rooted in the three most significant features of the multidimensional burst - the number of dimensions, prevalence and interleaving degree - able to reproduce the main media usage attitude. The other findings of the paper include a novel multidimensional burst detection algorithm and an insight analysis of the human media selection process.", "link"=>"http://www.mendeley.com/research/multidimensional-human-dynamics-mobile-phone-communications", "reader_count"=>21, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>2, "Researcher"=>4, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>8, "Student > Bachelor"=>3, "Lecturer"=>1, "Professor"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>2, "Researcher"=>4, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>8, "Student > Bachelor"=>3, "Lecturer"=>1, "Professor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>1, "Engineering"=>2, "Environmental Science"=>1, "Arts and Humanities"=>1, "Physics and Astronomy"=>1, "Psychology"=>3, "Social Sciences"=>4, "Computer Science"=>8}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>2}, "Social Sciences"=>{"Social Sciences"=>4}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Psychology"=>{"Psychology"=>3}, "Computer Science"=>{"Computer Science"=>8}, "Unspecified"=>{"Unspecified"=>1}, "Environmental Science"=>{"Environmental Science"=>1}, "Arts and Humanities"=>{"Arts and Humanities"=>1}}, "reader_count_by_country"=>{"Canada"=>1}, "group_count"=>1}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1611617"], "description"=>"<p>Mean of the percentage of events inside the bursts by varying the threshold from 1 to 60 seconds: (a) D 1, (b) D 2.</p>", "links"=>[], "tags"=>["Information technology", "data mining", "Network Analysis", "social networks", "Computerized simulations", "Computer modeling", "Systems science", "Complex systems", "Sociology", "communications", "Computational sociology", "detection"], "article_id"=>1119904, "categories"=>["Science Policy"], "users"=>["Christian Quadri", "Matteo Zignani", "Lorenzo Capra", "Sabrina Gaito", "Gian Paolo Rossi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0103183.g002", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Burst_detection_results_/1119904", "title"=>"Burst detection results.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-28 02:48:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/1611615"], "description"=>"<p>This example explains how the burst detection algorithm works. The algorithm correctly identifies the densest regions (black dashed boxes) avoiding to include the event in between (red solid line).</p>", "links"=>[], "tags"=>["Information technology", "data mining", "Network Analysis", "social networks", "Computerized simulations", "Computer modeling", "Systems science", "Complex systems", "Sociology", "communications", "Computational sociology"], "article_id"=>1119902, "categories"=>["Science Policy"], "users"=>["Christian Quadri", "Matteo Zignani", "Lorenzo Capra", "Sabrina Gaito", "Gian Paolo Rossi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0103183.g001", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Example_of_event_time_series_/1119902", "title"=>"Example of event time series.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-28 02:48:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/1611624"], "description"=>"<p>Bi-dimensional burst generative GSPN-model.</p>", "links"=>[], "tags"=>["Information technology", "data mining", "Network Analysis", "social networks", "Computerized simulations", "Computer modeling", "Systems science", "Complex systems", "Sociology", "communications", "Computational sociology", "generative"], "article_id"=>1119911, "categories"=>["Science Policy"], "users"=>["Christian Quadri", "Matteo Zignani", "Lorenzo Capra", "Sabrina Gaito", "Gian Paolo Rossi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0103183.g007", "stats"=>{"downloads"=>0, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Bi_dimensional_burst_generative_GSPN_model_/1119911", "title"=>"Bi-dimensional burst generative GSPN-model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-28 02:48:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/1611625"], "description"=>"<p>(a) Prevalence distributions. (b) Interleaving degree distribution. We use the following parameter values: and .</p>", "links"=>[], "tags"=>["Information technology", "data mining", "Network Analysis", "social networks", "Computerized simulations", "Computer modeling", "Systems science", "Complex systems", "Sociology", "communications", "Computational sociology"], "article_id"=>1119912, "categories"=>["Science Policy"], "users"=>["Christian Quadri", "Matteo Zignani", "Lorenzo Capra", "Sabrina Gaito", "Gian Paolo Rossi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0103183.g008", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparative_analysis_in_case_of_burst_length_13_/1119912", "title"=>"Comparative analysis in case of burst length 13.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-28 02:48:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/1611623"], "description"=>"<p>Graphical representation of the discrete time Markov Chain (MC) in case of burst length . MC states are described by four integer variables, (length), (prevalence), (switches) and (last). denotes the length of (sub)sequences represented by a state (in the picture values of correspond to the depth level in the DAG – Direct Acyclic Graph). and indicate the number of ‘1’s and the number of switches, respectively. denotes the last occurred event and allows tracking switches. Each state, but , is annotated with some variables' bindings (top) and the (sub)sequences it represents (bottom). States drawn in bold correspond to aggregates of sequences. The parameters and represent the probabilities that the next event coincides with the last one. In order to make the MC irreducible, we assume that each final state () brings the system back to state with probability 1 (these connections are omitted in the picture).</p>", "links"=>[], "tags"=>["Information technology", "data mining", "Network Analysis", "social networks", "Computerized simulations", "Computer modeling", "Systems science", "Complex systems", "Sociology", "communications", "Computational sociology", "markov"], "article_id"=>1119910, "categories"=>["Science Policy"], "users"=>["Christian Quadri", "Matteo Zignani", "Lorenzo Capra", "Sabrina Gaito", "Gian Paolo Rossi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0103183.g006", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Discrete_time_Markov_chain_/1119910", "title"=>"Discrete time Markov chain.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-28 02:48:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/1611620"], "description"=>"<p>CDF of the Spearman correlation coefficient computed on D 2 considering sequences with more than three distinct receivers. We report the distribution for both day (mean 0.21, median 0.24 and std 0.36) and burst (mean 0.12, median 0.17 and std 0.50) level.</p>", "links"=>[], "tags"=>["Information technology", "data mining", "Network Analysis", "social networks", "Computerized simulations", "Computer modeling", "Systems science", "Complex systems", "Sociology", "communications", "Computational sociology", "spearman"], "article_id"=>1119907, "categories"=>["Science Policy"], "users"=>["Christian Quadri", "Matteo Zignani", "Lorenzo Capra", "Sabrina Gaito", "Gian Paolo Rossi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0103183.g004", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Distribution_of_Spearman_correlation_coefficient_/1119907", "title"=>"Distribution of Spearman correlation coefficient.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-28 02:48:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/1611627"], "description"=>"<p>(a) Prevalence distribution. (b) Interleaving degree distributions. We use the following parameters values: and .</p>", "links"=>[], "tags"=>["Information technology", "data mining", "Network Analysis", "social networks", "Computerized simulations", "Computer modeling", "Systems science", "Complex systems", "Sociology", "communications", "Computational sociology", "dataset"], "article_id"=>1119914, "categories"=>["Science Policy"], "users"=>["Christian Quadri", "Matteo Zignani", "Lorenzo Capra", "Sabrina Gaito", "Gian Paolo Rossi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0103183.g009", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparative_analysis_on_dataset_D_1_/1119914", "title"=>"Comparative analysis on dataset D 1.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-28 02:48:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/1611622"], "description"=>"<p>CDF of the Spearman correlation coefficient computed on D 2 by grouping burst by type and considering sequences with more than three distinct receivers.</p>", "links"=>[], "tags"=>["Information technology", "data mining", "Network Analysis", "social networks", "Computerized simulations", "Computer modeling", "Systems science", "Complex systems", "Sociology", "communications", "Computational sociology", "spearman", "coefficient"], "article_id"=>1119909, "categories"=>["Science Policy"], "users"=>["Christian Quadri", "Matteo Zignani", "Lorenzo Capra", "Sabrina Gaito", "Gian Paolo Rossi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0103183.g005", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Distribution_of_Spearman_correlation_coefficient_per_burst_type_/1119909", "title"=>"Distribution of Spearman correlation coefficient per burst type.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-28 02:48:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/1611618"], "description"=>"<p>Interleaving degree computed on interleaved burst. (a) D1: mean 0.32, median 0.29, and standard deviation 0.20. (b) D2: mean 0.36, median 0.31, and standard deviation 0.23.</p>", "links"=>[], "tags"=>["Information technology", "data mining", "Network Analysis", "social networks", "Computerized simulations", "Computer modeling", "Systems science", "Complex systems", "Sociology", "communications", "Computational sociology", "interleaved"], "article_id"=>1119905, "categories"=>["Science Policy"], "users"=>["Christian Quadri", "Matteo Zignani", "Lorenzo Capra", "Sabrina Gaito", "Gian Paolo Rossi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0103183.g003", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Histogram_of_interleaved_degree_/1119905", "title"=>"Histogram of interleaved degree.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-28 02:48:50"}

PMC Usage Stats | Further Information

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  • {"unique-ip"=>"10", "full-text"=>"5", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"9"}
  • {"unique-ip"=>"7", "full-text"=>"9", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"10"}
  • {"unique-ip"=>"9", "full-text"=>"10", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"11"}
  • {"unique-ip"=>"3", "full-text"=>"4", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"1"}
  • {"unique-ip"=>"12", "full-text"=>"7", "pdf"=>"9", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"3"}
  • {"unique-ip"=>"12", "full-text"=>"5", "pdf"=>"8", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"1"}
  • {"unique-ip"=>"19", "full-text"=>"13", "pdf"=>"8", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"12"}
  • {"unique-ip"=>"11", "full-text"=>"4", "pdf"=>"9", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"11"}
  • {"unique-ip"=>"15", "full-text"=>"5", "pdf"=>"9", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"9"}
  • {"unique-ip"=>"15", "full-text"=>"7", "pdf"=>"8", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"4"}
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Relative Metric

{"start_date"=>"2014-01-01T00:00:00Z", "end_date"=>"2014-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences", "average_usage"=>[291]}, {"subject_area"=>"/Biology and life sciences/Behavior", "average_usage"=>[333]}, {"subject_area"=>"/Computer and information sciences", "average_usage"=>[327, 511]}, {"subject_area"=>"/Engineering and technology", "average_usage"=>[282]}, {"subject_area"=>"/Engineering and technology/Telecommunications", "average_usage"=>[435, 569, 693]}, {"subject_area"=>"/Physical sciences", "average_usage"=>[271]}, {"subject_area"=>"/Physical sciences/Mathematics", "average_usage"=>[286]}, {"subject_area"=>"/Social sciences/Sociology", "average_usage"=>[365]}]}
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