Geographies of an Online Social Network
Publication Date
September 11, 2015
Journal
PLOS ONE
Authors
Balázs Lengyel, Attila Varga, Bence Ságvári, Ákos Jakobi, et al
Volume
10
Issue
9
Pages
e0137248
DOI
https://dx.plos.org/10.1371/journal.pone.0137248
Publisher URL
http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0137248
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/26359668
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4567269
Europe PMC
http://europepmc.org/abstract/MED/26359668
Web of Science
000361043100027
Scopus
84945320273
Mendeley
http://www.mendeley.com/research/geographies-online-social-network
Events
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Mendeley | Further Information

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Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2258719"], "description"=>"<p>Few large communities are identified in the <math><mrow><msubsup><mi>w</mi><mrow><mi>i</mi><mi>j</mi></mrow><mrow><mrow><mo>(</mo><mn>1</mn><mo>)</mo></mrow></mrow></msubsup></mrow></math> network compared to the <math><mrow><msubsup><mi>w</mi><mrow><mi>i</mi><mi>j</mi></mrow><mrow><mrow><mo>(</mo><mn>2</mn><mo>)</mo></mrow></mrow></msubsup></mrow></math> network. The five runs of the Louvain method finds exactly the same community structure in the <math><mrow><msubsup><mi>w</mi><mrow><mi>i</mi><mi>j</mi></mrow><mrow><mrow><mo>(</mo><mn>1</mn><mo>)</mo></mrow></mrow></msubsup></mrow></math> network, therefore we report the result of only one run. The community finding algorithm produced distinct community structures in the <math><mrow><msubsup><mi>w</mi><mrow><mi>i</mi><mi>j</mi></mrow><mrow><mrow><mo>(</mo><mn>2</mn><mo>)</mo></mrow></mrow></msubsup></mrow></math> network; therefore we report all five runs (the number after the hyphen denotes the sequence of the run). The pair-wise Cramer’s V index in <math><mrow><msubsup><mi>w</mi><mrow><mi>i</mi><mi>j</mi></mrow><mrow><mrow><mo>(</mo><mn>2</mn><mo>)</mo></mrow></mrow></msubsup></mrow></math> network is always above 0.95.</p><p>Network modularity.</p>", "links"=>[], "tags"=>["distance dependence", "Online Social Network", "phone communication networks"], "article_id"=>1540064, "categories"=>["Uncategorised"], "users"=>["Balázs Lengyel", "Attila Varga", "Bence Ságvári", "Ákos Jakobi", "János Kertész"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0137248.t002", "stats"=>{"downloads"=>3, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Network_modularity_/1540064", "title"=>"Network modularity.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-09-11 03:09:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/2258717"], "description"=>"<p>(A) The strongest 0.3% of all edges are depicted in <math><mrow><msubsup><mi>w</mi><mrow><mi>i</mi><mi>j</mi></mrow><mrow><mrow><mo>(</mo><mn>1</mn><mo>)</mo></mrow></mrow></msubsup></mrow></math> network (4,081 edges). (B) The Louvain method finds 5 modules in the <math><mrow><msubsup><mi>w</mi><mrow><mi>i</mi><mi>j</mi></mrow><mrow><mrow><mo>(</mo><mn>1</mn><mo>)</mo></mrow></mrow></msubsup></mrow></math> network; towns belonging to the same module are depicted with same colors. (C) The strongest 0.3% of all edges are depicted in <math><mrow><msubsup><mi>w</mi><mrow><mi>i</mi><mi>j</mi></mrow><mrow><mrow><mo>(</mo><mn>2</mn><mo>)</mo></mrow></mrow></msubsup></mrow></math> network (4,081 edges). (D) The illustrated community structure contains 14 modules in the <math><mrow><msubsup><mi>w</mi><mrow><mi>i</mi><mi>j</mi></mrow><mrow><mrow><mo>(</mo><mn>2</mn><mo>)</mo></mrow></mrow></msubsup></mrow></math> network. This community structure has the highest modularity index out of the Louvain algorithm runs we present in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0137248#pone.0137248.t002\" target=\"_blank\">Table 2</a>. Towns belonging to the same module are depicted with same colors. Created by own data, with base map of OpenStreetMap cartography licensed as CC BY-SA.</p>", "links"=>[], "tags"=>["distance dependence", "Online Social Network", "phone communication networks"], "article_id"=>1540062, "categories"=>["Uncategorised"], "users"=>["Balázs Lengyel", "Attila Varga", "Bence Ságvári", "Ákos Jakobi", "János Kertész"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0137248.g004", "stats"=>{"downloads"=>2, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Spatial_structure_and_modularity_/1540062", "title"=>"Spatial structure and modularity.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-09-11 03:09:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/2258714"], "description"=>"<p>Weighted degree distribution of town network, loops excluded. Weights are the total number of user-to-user links between two towns. Weighted degrees were binned into 10<sup>4</sup> intervals for <i>P</i>(<i>w</i>) calculation; blue hollow circle symbols represent the bins; black plus symbols represent the mean of weighted degree by each unique value of <i>P</i>(<i>w</i>). The slope of the solid line is -1.4, which fits <i>P</i>(<i>w</i>) values with R<sup>2</sup> = 0.66.</p>", "links"=>[], "tags"=>["distance dependence", "Online Social Network", "phone communication networks"], "article_id"=>1540059, "categories"=>["Uncategorised"], "users"=>["Balázs Lengyel", "Attila Varga", "Bence Ságvári", "Ákos Jakobi", "János Kertész"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0137248.g001", "stats"=>{"downloads"=>3, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Degree_distribution_/1540059", "title"=>"Degree distribution.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-09-11 03:09:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/2258740", "https://ndownloader.figshare.com/files/2258741", "https://ndownloader.figshare.com/files/2258742", "https://ndownloader.figshare.com/files/2258743"], "description"=>"<div><p>How is online social media activity structured in the geographical space? Recent studies have shown that in spite of earlier visions about the “death of distance”, physical proximity is still a major factor in social tie formation and maintenance in virtual social networks. Yet, it is unclear, what are the characteristics of the distance dependence in online social networks. In order to explore this issue the complete network of the former major Hungarian online social network is analyzed. We find that the distance dependence is weaker for the online social network ties than what was found earlier for phone communication networks. For a further analysis we introduced a coarser granularity: We identified the settlements with the nodes of a network and assigned two kinds of weights to the links between them. When the weights are proportional to the number of contacts we observed weakly formed, but spatially based modules resemble to the borders of macro-regions, the highest level of regional administration in the country. If the weights are defined relative to an uncorrelated null model, the next level of administrative regions, counties are reflected.</p></div>", "links"=>[], "tags"=>["distance dependence", "Online Social Network", "phone communication networks"], "article_id"=>1540072, "categories"=>["Uncategorised"], "users"=>["Balázs Lengyel", "Attila Varga", "Bence Ságvári", "Ákos Jakobi", "János Kertész"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0137248.s001", "https://dx.doi.org/10.1371/journal.pone.0137248.s002", "https://dx.doi.org/10.1371/journal.pone.0137248.s003", "https://dx.doi.org/10.1371/journal.pone.0137248.s004"], "stats"=>{"downloads"=>4, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Geographies_of_an_Online_Social_Network_/1540072", "title"=>"Geographies of an Online Social Network", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-09-11 03:09:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/2258716"], "description"=>"<p>(A) The distribution of <math><mrow><msubsup><mi>w</mi><mrow><mi>i</mi><mi>j</mi></mrow><mrow><mrow><mo>(</mo><mn>1</mn><mo>)</mo></mrow></mrow></msubsup></mrow></math> weights is unimodal and with a maximum at the value of 1. The tail ends at 7.68 indicating that a large fraction of the ties represent very low number of connections compared to a small set of high links indicating large number of personal ties. (B) Node strength <i>s</i><sup>(1)</sup> rises as population increases. The capital, Budapest with its 2 million inhabitants is an outlier. (C) The heat map of the density of <math><mrow><msubsup><mi>w</mi><mrow><mi>i</mi><mi>j</mi></mrow><mrow><mrow><mo>(</mo><mn>1</mn><mo>)</mo></mrow></mrow></msubsup></mrow></math> as a function of <math><mrow><msubsup><mi>w</mi><mrow><mi>i</mi><mi>j</mi></mrow><mrow><mrow><mo>(</mo><mn>1</mn><mo>)</mo></mrow></mrow></msubsup></mrow></math> and the distance between towns <i>i</i> and <i>j</i> shows a complex distribution that is dominated by a large number of weak ties between distant locations. (D) <math><mrow><msubsup><mi>w</mi><mrow><mi>i</mi><mi>j</mi></mrow><mrow><mrow><mo>(</mo><mn>2</mn><mo>)</mo></mrow></mrow></msubsup></mrow></math> has a unimodal distribution with values between –2.61 and 3.29 and with a modus at –0.77. (E) Node strength <i>s</i><sup>(2)</sup> decreases as population increases but fluctuation is high across large towns. Budapest is again an outlier. (F) The heat map of the density of <math><mrow><msubsup><mi>w</mi><mrow><mi>i</mi><mi>j</mi></mrow><mrow><mrow><mo>(</mo><mn>2</mn><mo>)</mo></mrow></mrow></msubsup></mrow></math> as a function of <math><mrow><msubsup><mi>w</mi><mrow><mi>i</mi><mi>j</mi></mrow><mrow><mrow><mo>(</mo><mn>2</mn><mo>)</mo></mrow></mrow></msubsup></mrow></math> and the distance between towns <i>i</i> and <i>j</i> illustrates that the highest edge weights are between towns that are in geographical proximity.</p>", "links"=>[], "tags"=>["distance dependence", "Online Social Network", "phone communication networks"], "article_id"=>1540061, "categories"=>["Uncategorised"], "users"=>["Balázs Lengyel", "Attila Varga", "Bence Ságvári", "Ákos Jakobi", "János Kertész"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0137248.g003", "stats"=>{"downloads"=>6, "page_views"=>26, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Edge_weight_and_node_strength_distributions_/1540061", "title"=>"Edge weight and node strength distributions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-09-11 03:09:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/2258715"], "description"=>"<p>Probability <i>P</i>(<i>d</i>) is plotted as function of distance on a log-log scale. The straight line depicts a power-law <i>d</i><sup>-0.6</sup>.</p>", "links"=>[], "tags"=>["distance dependence", "Online Social Network", "phone communication networks"], "article_id"=>1540060, "categories"=>["Uncategorised"], "users"=>["Balázs Lengyel", "Attila Varga", "Bence Ságvári", "Ákos Jakobi", "János Kertész"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0137248.g002", "stats"=>{"downloads"=>3, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_probability_of_links_as_function_of_distance_/1540060", "title"=>"The probability of links as function of distance.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-09-11 03:09:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/2258718"], "description"=>"<p>Data for the user-level network take into account all iWiW links. Data for the town-level network are aggregated: whenever there is at least one user-level link between two persons in different towns there is a link between those towns. As there are always intra-town links, the number of loops in the town network equals with the population of towns.</p><p>Aggregation of the iWiW network to a town-level.</p>", "links"=>[], "tags"=>["distance dependence", "Online Social Network", "phone communication networks"], "article_id"=>1540063, "categories"=>["Uncategorised"], "users"=>["Balázs Lengyel", "Attila Varga", "Bence Ságvári", "Ákos Jakobi", "János Kertész"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0137248.t001", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Aggregation_of_the_iWiW_network_to_a_town_level_/1540063", "title"=>"Aggregation of the iWiW network to a town-level.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-09-11 03:09:39"}

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  • {"unique-ip"=>"19", "full-text"=>"15", "pdf"=>"6", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"11"}
  • {"unique-ip"=>"12", "full-text"=>"11", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"12"}
  • {"unique-ip"=>"13", "full-text"=>"13", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"1", "year"=>"2021", "month"=>"1"}

Relative Metric

{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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