Correlated Dynamics in Egocentric Communication Networks
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{"title"=>"Correlated dynamics in egocentric communication networks", "type"=>"journal", "authors"=>[{"first_name"=>"Márton", "last_name"=>"Karsai", "scopus_author_id"=>"12784099900"}, {"first_name"=>"Kimmo", "last_name"=>"Kaski", "scopus_author_id"=>"26643315700"}, {"first_name"=>"János", "last_name"=>"Kertész", "scopus_author_id"=>"35473453300"}], "year"=>2012, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84863745321", "isbn"=>"0011-3891", "arxiv"=>"1202.3062", "doi"=>"10.1371/journal.pone.0040612", "pui"=>"365232249", "sgr"=>"84863745321", "issn"=>"19326203", "pmid"=>"1000090690"}, "id"=>"37366d7e-e2d1-328e-a8a6-5e04388362c6", "abstract"=>"We investigate the communication sequences of millions of people through two different channels and analyze the fine grained temporal structure of correlated event trains induced by single individuals. By focusing on correlations between the heterogeneous dynamics and the topology of egocentric networks we find that the bursty trains usually evolve for pairs of individuals rather than for the ego and his/her several neighbors thus burstiness is a property of the links rather than of the nodes. We compare the directional balance of calls and short messages within bursty trains to the average on the actual link and show that for the trains of voice calls the imbalance is significantly enhanced, while for short messages the balance within the trains increases. These effects can be partly traced back to the technological constrains (for short messages) and partly to the human behavioral features (voice calls). We define a model that is able to reproduce the empirical results and may help us to understand better the mechanisms driving technology mediated human communication dynamics.", "link"=>"http://www.mendeley.com/research/correlated-dynamics-egocentric-communication-networks", "reader_count"=>36, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>1, "Student > Doctoral Student"=>2, "Researcher"=>6, "Student > Ph. D. Student"=>13, "Student > Master"=>8, "Other"=>1, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>2, "Professor"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>1, "Student > Doctoral Student"=>2, "Researcher"=>6, "Student > Ph. D. Student"=>13, "Student > Master"=>8, "Other"=>1, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>2, "Professor"=>1}, "reader_count_by_subject_area"=>{"Engineering"=>3, "Unspecified"=>2, "Environmental Science"=>2, "Mathematics"=>4, "Neuroscience"=>1, "Philosophy"=>1, "Business, Management and Accounting"=>1, "Physics and Astronomy"=>9, "Psychology"=>2, "Social Sciences"=>6, "Computer Science"=>5}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>3}, "Neuroscience"=>{"Neuroscience"=>1}, "Social Sciences"=>{"Social Sciences"=>6}, "Physics and Astronomy"=>{"Physics and Astronomy"=>9}, "Psychology"=>{"Psychology"=>2}, "Computer Science"=>{"Computer Science"=>5}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Mathematics"=>{"Mathematics"=>4}, "Unspecified"=>{"Unspecified"=>2}, "Environmental Science"=>{"Environmental Science"=>2}, "Philosophy"=>{"Philosophy"=>1}}, "reader_count_by_country"=>{"Netherlands"=>1, "Sweden"=>1, "United States"=>1, "Italy"=>1, "Switzerland"=>1, "Spain"=>1, "Estonia"=>1}, "group_count"=>4}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/610128"], "description"=>"<p>(a) Overall activity: darker the color larger the number of calls at the given hour. First line (green) denotes the overall in/out communication of the selected user, while the lines and demonstrate the communication on each active edges. (b,c,d) In- (red) and outgoing (blue) activity on given edges during selected time periods. The width of the coloured strips reflects the length of the corresponding calls. The selected periods are between 8AM and 1AM on the 5th, 16th and 29th days.</p>", "links"=>[], "tags"=>["egocentric"], "article_id"=>280629, "categories"=>["Information And Computing Sciences", "Mathematics", "Physics"], "users"=>["Márton Karsai", "Kimmo Kaski", "János Kertész"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0040612.g001", "stats"=>{"downloads"=>0, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Call_activity_in_an_egocentric_network_/280629", "title"=>"Call activity in an egocentric network.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-07-12 00:10:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/610242"], "description"=>"<p>(a) Distributions of length of bursty periods of outgoing events of nodes towards all the neighbours (solid lines) and to the direction of single neighbours (dashed lines). The measurement was repeated with time windows and seconds using the original event sequence (original) and after the inter-event times were shuffled (random). Inset: Inter-event time distributions between consecutive outgoing events of the same user towards all neighbours (solid line) and between outgoing events on a single link (dashed line). (b) The same measurements repeated for outgoing short messages with and 600 seconds.</p>", "links"=>[], "tags"=>["distributions", "sms"], "article_id"=>280738, "categories"=>["Information And Computing Sciences", "Mathematics", "Physics"], "users"=>["Márton Karsai", "Kimmo Kaski", "János Kertész"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0040612.g002", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Characteristic_distributions_of_call_and_SMS_sequences_/280738", "title"=>"Characteristic distributions of call and SMS sequences.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-07-12 00:12:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/318823"], "description"=>"<div><p>We investigate the communication sequences of millions of people through two different channels and analyse the fine grained temporal structure of correlated event trains induced by single individuals. By focusing on correlations between the heterogeneous dynamics and the topology of egocentric networks we find that the bursty trains usually evolve for pairs of individuals rather than for the ego and his/her several neighbours, thus burstiness is a property of the links rather than of the nodes. We compare the directional balance of calls and short messages within bursty trains to the average on the actual link and show that for the trains of voice calls the imbalance is significantly enhanced, while for short messages the balance within the trains increases. These effects can be partly traced back to the technological constraints (for short messages) and partly to the human behavioural features (voice calls). We define a model that is able to reproduce the empirical results and may help us to understand better the mechanisms driving technology mediated human communication dynamics.</p> </div>", "links"=>[], "tags"=>["correlated", "egocentric", "networks"], "article_id"=>122927, "categories"=>["Information And Computing Sciences", "Mathematics", "Physics"], "users"=>["Márton Karsai", "Kimmo Kaski", "János Kertész"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0040612", "stats"=>{"downloads"=>1, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Correlated_Dynamics_in_Egocentric_Communication_Networks/122927", "title"=>"Correlated Dynamics in Egocentric Communication Networks", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-07-12 00:48:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/610506"], "description"=>"<p>(a) Illustrative definition of the model. Here events are simulated between two people <i>A</i> and <i>B</i>. The dynamics and direction of the events are determined by probabilities , and defined in the text. (b) distributions of empirical call trains (red circles) with on edges with and in corresponding model trains (black circles). The same functions are shown for SMS trains (blue and yellow squares accordingly). (c) Balance values calculated for empirical call (red circles) and SMS (blue squares) trains and in corresponding model processes (red and yellow symbols). Balance values of independent trains are also shown (green triangles) calculated by Eq.5 with .</p>", "links"=>[], "tags"=>["computer science", "physics", "mathematics"], "article_id"=>281003, "categories"=>["Information And Computing Sciences", "Mathematics", "Physics"], "users"=>["Márton Karsai", "Kimmo Kaski", "János Kertész"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0040612.g005", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_definition_and_results_/281003", "title"=>"Model definition and results.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-07-12 00:16:43"}
  • {"files"=>["https://ndownloader.figshare.com/files/610422"], "description"=>"<p>(a) Average edge balance values calculated for trains with the same size for calls (orange circles) and SMS (brown squares). (b) Average train balance values for trains of the same size in case of calls (red circles) and SMS (blue squares). A similar average was calculated for corresponding independent event trains (yellow circles for calls and green squares for SMS). (c) Average and measured in call sequences where events with duration smaller than were removed. (d) The difference of the empirical and independent values in call sequences where events with duration smaller than were removed.</p>", "links"=>[], "tags"=>["measures", "sms"], "article_id"=>280917, "categories"=>["Information And Computing Sciences", "Mathematics", "Physics"], "users"=>["Márton Karsai", "Kimmo Kaski", "János Kertész"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0040612.g004", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Directed_balance_measures_in_call_and_SMS_sequences_/280917", "title"=>"Directed balance measures in call and SMS sequences.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-07-12 00:15:17"}
  • {"files"=>["https://ndownloader.figshare.com/files/610332"], "description"=>"<p>(a, b) The distribution and average values of the ratio ( being the number of neighbours, whom an individual called in a bursty train) for each train size. Trains were detected with () for calls (SMS). The pointed and solid lines denote the limiting case and , while dashed line belongs to . (c, d) Cumulative fraction of the number of bursty neighbours (BN) for trains with size <i>E</i> for calls and SMS. (e, f) Average of the maximum number of events directed to the same user within a bursty period in case of calls and SMS.</p>", "links"=>[], "tags"=>["bursty"], "article_id"=>280823, "categories"=>["Information And Computing Sciences", "Mathematics", "Physics"], "users"=>["Márton Karsai", "Kimmo Kaski", "János Kertész"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0040612.g003", "stats"=>{"downloads"=>0, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Measures_of_single_link_bursty_trains_/280823", "title"=>"Measures of single link bursty trains.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-07-12 00:13:43"}

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