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Parsing Social Network Survey Data from Hidden Populations Using Stochastic Context-Free Grammars

Publication Date: September 07, 2009
Journal: PLOS ONE
Authors: Art F. Y. Poon, Kimberly C. Brouwer, Steffanie A. Strathdee, Michelle Firestone Cruz, et al
Volume: 4
Issue: 9
Pages: e6777
DOI: https://dx.plos.org/10.1371/journal.pone.0006777
Publisher URL: http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0006777
PubMed: http://www.ncbi.nlm.nih.gov/pubmed/19738904
PubMed Central: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2734164
Europe PMC: http://europepmc.org/abstract/MED/19738904
Web of Science: 000269622300002
Scopus: 70249101019
Mendeley: http://www.mendeley.com/research/parsing-social-network-survey-data-hidden-populations-using-stochastic-contextfree-grammars-6

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Mendeley 2725 Apr 02:15 UTC

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{"title"=>"Parsing Social Network Survey Data from Hidden Populations Using Stochastic Context-Free Grammars", "type"=>"journal", "authors"=>[{"first_name"=>"Art F Y", "last_name"=>"Poon"}, {"first_name"=>"Kimberly C.", "last_name"=>"Brouwer"}, {"first_name"=>"Steffanie A.", "last_name"=>"Strathdee"}, {"first_name"=>"Michelle", "last_name"=>"Firestone-Cruz"}, {"first_name"=>"Remedios M.", "last_name"=>"Lozada"}, {"first_name"=>"Sergei L.", "last_name"=>"Kosakovsky Pond"}, {"first_name"=>"Douglas D.", "last_name"=>"Heckathorn"}, {"first_name"=>"Simon D W", "last_name"=>"Frost"}], "year"=>2009, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"1932-6203", "isbn"=>"1932-6203 (Electronic)\\n1932-6203 (Linking)", "pmid"=>"19738904", "doi"=>"10.1371/journal.pone.0006777"}, "id"=>"55d47619-ce19-3ac4-8575-d1be973870c6", "abstract"=>"BACKGROUND: Human populations are structured by social networks, in which individuals tend to form relationships based on shared attributes. Certain attributes that are ambiguous, stigmatized or illegal can create a OhiddenO population, so-called because its members are difficult to identify. Many hidden populations are also at an elevated risk of exposure to infectious diseases. Consequently, public health agencies are presently adopting modern survey techniques that traverse social networks in hidden populations by soliciting individuals to recruit their peers, e.g., respondent-driven sampling (RDS). The concomitant accumulation of network-based epidemiological data, however, is rapidly outpacing the development of computational methods for analysis. Moreover, current analytical models rely on unrealistic assumptions, e.g., that the traversal of social networks can be modeled by a Markov chain rather than a branching process.\\n\\nMETHODOLOGY/PRINCIPAL FINDINGS: Here, we develop a new methodology based on stochastic context-free grammars (SCFGs), which are well-suited to modeling tree-like structure of the RDS recruitment process. We apply this methodology to an RDS case study of injection drug users (IDUs) in Tijuana, México, a hidden population at high risk of blood-borne and sexually-transmitted infections (i.e., HIV, hepatitis C virus, syphilis). Survey data were encoded as text strings that were parsed using our custom implementation of the inside-outside algorithm in a publicly-available software package (HyPhy), which uses either expectation maximization or direct optimization methods and permits constraints on model parameters for hypothesis testing. We identified significant latent variability in the recruitment process that violates assumptions of Markov chain-based methods for RDS analysis: firstly, IDUs tended to emulate the recruitment behavior of their own recruiter; and secondly, the recruitment of like peers (homophily) was dependent on the number of recruits.\\n\\nCONCLUSIONS: SCFGs provide a rich probabilistic language that can articulate complex latent structure in survey data derived from the traversal of social networks. Such structure that has no representation in Markov chain-based models can interfere with the estimation of the composition of hidden populations if left unaccounted for, raising critical implications for the prevention and control of infectious disease epidemics.", "link"=>"http://www.mendeley.com/research/parsing-social-network-survey-data-hidden-populations-using-stochastic-contextfree-grammars-6", "reader_count"=>25, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>4, "Librarian"=>1, "Student > Doctoral Student"=>3, "Researcher"=>6, "Student > Ph. D. Student"=>4, "Student > Postgraduate"=>2, "Student > Master"=>2, "Student > Bachelor"=>1, "Professor"=>2}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>4, "Librarian"=>1, "Student > Doctoral Student"=>3, "Researcher"=>6, "Student > Ph. D. Student"=>4, "Student > Postgraduate"=>2, "Student > Master"=>2, "Student > Bachelor"=>1, "Professor"=>2}, "reader_count_by_subject_area"=>{"Unspecified"=>4, "Mathematics"=>1, "Medicine and Dentistry"=>5, "Agricultural and Biological Sciences"=>1, "Neuroscience"=>1, "Business, Management and Accounting"=>1, "Psychology"=>3, "Social Sciences"=>4, "Computer Science"=>5}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>5}, "Neuroscience"=>{"Neuroscience"=>1}, "Social Sciences"=>{"Social Sciences"=>4}, "Psychology"=>{"Psychology"=>3}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>1}, "Computer Science"=>{"Computer Science"=>5}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>4}}, "reader_count_by_country"=>{"United States"=>3, "Brazil"=>1, "United Kingdom"=>1, "Italy"=>1, "Thailand"=>1}, "group_count"=>2}

ORCID 230 Apr 12:59 UTC

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Figshare 3010 Apr 09:52 UTC

Figshare

{"files"=>["https://ndownloader.figshare.com/files/884768"], "description"=>"<p>Strings generated by an SCFG are depicted on the left-hand side. is the start symbol from which all strings are derived, corresponding to the initiation of an RDS study; and are non-terminal symbols for different types of respondents whose recruitment outcomes are unresolved; and and are terminal symbols for respondents whose recruitment outcomes are resolved. A successful recruitment is indicated by a substring enclosed in parentheses to the immediate right of the originating terminal symbol. To represent the termination of an RDS study, the remaining non-terminal symbols are rewritten as lower-case terminal symbols, <i>e.g.</i>, .</p>", "links"=>[], "tags"=>["stochastic", "context-free", "grammar", "multitype", "branching"], "article_id"=>555213, "categories"=>["Biological Sciences", "Medicine", "Biotechnology"], "users"=>["Art F. Y. Poon", "Kimberly C. Brouwer", "Steffanie A. Strathdee", "Michelle Firestone-Cruz", "Remedios M. Lozada", "Sergei L. Kosakovsky Pond", "Douglas D. Heckathorn", "Simon D. W. Frost"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0006777.g001", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Relating_a_stochastic_context_free_grammar_SCFG_to_the_multitype_branching_process_of_RDS_/555213", "title"=>"Relating a stochastic context-free grammar (SCFG) to the multitype branching process of RDS.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-09-07 01:26:53"}
{"files"=>["https://ndownloader.figshare.com/files/439091", "https://ndownloader.figshare.com/files/439141", "https://ndownloader.figshare.com/files/439183", "https://ndownloader.figshare.com/files/439246", "https://ndownloader.figshare.com/files/439297"], "description"=>"<div><h3>Background</h3><p>Human populations are structured by social networks, in which individuals tend to form relationships based on shared attributes. Certain attributes that are ambiguous, stigmatized or illegal can create a ÔhiddenÕ population, so-called because its members are difficult to identify. Many hidden populations are also at an elevated risk of exposure to infectious diseases. Consequently, public health agencies are presently adopting modern survey techniques that traverse social networks in hidden populations by soliciting individuals to recruit their peers, <em>e.g.</em>, respondent-driven sampling (RDS). The concomitant accumulation of network-based epidemiological data, however, is rapidly outpacing the development of computational methods for analysis. Moreover, current analytical models rely on unrealistic assumptions, <em>e.g.</em>, that the traversal of social networks can be modeled by a Markov chain rather than a branching process.</p><h3>Methodology/Principal Findings</h3><p>Here, we develop a new methodology based on stochastic context-free grammars (SCFGs), which are well-suited to modeling tree-like structure of the RDS recruitment process. We apply this methodology to an RDS case study of injection drug users (IDUs) in Tijuana, México, a hidden population at high risk of blood-borne and sexually-transmitted infections (<em>i.e.</em>, HIV, hepatitis C virus, syphilis). Survey data were encoded as text strings that were parsed using our custom implementation of the inside-outside algorithm in a publicly-available software package (HyPhy), which uses either expectation maximization or direct optimization methods and permits constraints on model parameters for hypothesis testing. We identified significant latent variability in the recruitment process that violates assumptions of Markov chain-based methods for RDS analysis: firstly, IDUs tended to emulate the recruitment behavior of their own recruiter; and secondly, the recruitment of like peers (homophily) was dependent on the number of recruits.</p><h3>Conclusions</h3><p>SCFGs provide a rich probabilistic language that can articulate complex latent structure in survey data derived from the traversal of social networks. Such structure that has no representation in Markov chain-based models can interfere with the estimation of the composition of hidden populations if left unaccounted for, raising critical implications for the prevention and control of infectious disease epidemics.</p></div>", "links"=>[], "tags"=>["parsing", "populations", "stochastic", "context-free", "grammars"], "article_id"=>146572, "categories"=>["Biological Sciences", "Medicine", "Biotechnology"], "users"=>["Art F. Y. Poon", "Kimberly C. Brouwer", "Steffanie A. Strathdee", "Michelle Firestone-Cruz", "Remedios M. Lozada", "Sergei L. Kosakovsky Pond", "Douglas D. Heckathorn", "Simon D. W. Frost"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0006777.s001", "https://dx.doi.org/10.1371/journal.pone.0006777.s002", "https://dx.doi.org/10.1371/journal.pone.0006777.s003", "https://dx.doi.org/10.1371/journal.pone.0006777.s004", "https://dx.doi.org/10.1371/journal.pone.0006777.s005"], "stats"=>{"downloads"=>19, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Parsing_Social_Network_Survey_Data_from_Hidden_Populations_Using_Stochastic_Context_Free_Grammars/146572", "title"=>"Parsing Social Network Survey Data from Hidden Populations Using Stochastic Context-Free Grammars", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2009-09-07 01:49:32"}
{"files"=>["https://ndownloader.figshare.com/files/884949"], "description"=>"<p>( <i>A</i>) Histograms depict the inferred probability distribution for the number of recruits per respondent under the corresponding model. The dotted line histogram indicates the distribution in recruitment ability for respondents in the same hidden state as seed individuals. The mean number of recruits per respondent is indicated for each distribution by an inverted triangle along the x-axis. Plots are separated by <i>P</i>-values obtained from likelihood ratio tests between adjacent models (always favoring the right-hand side). ( <i>B</i>) Contrasting projected composition of samples based on Markov chain and multitype branching process (tree) models of RDS, in which the latter included hidden states to model variation among respondents not explained by the corresponding visible attribute (<i>viz.</i>, reporting prior use of methamphetamine, syphilis serostatus, and reporting injection drug use at home in the past six months). Frequencies correspond to the proportion of respondents in the ‘positive’ visible state (<i>i.e.</i>, using methamphetamines, syphilis seropositive, or using drugs at home).</p>", "links"=>[], "tags"=>["predictions", "recruitment"], "article_id"=>555401, "categories"=>["Biological Sciences", "Medicine", "Biotechnology"], "users"=>["Art F. Y. Poon", "Kimberly C. Brouwer", "Steffanie A. Strathdee", "Michelle Firestone-Cruz", "Remedios M. Lozada", "Sergei L. Kosakovsky Pond", "Douglas D. Heckathorn", "Simon D. W. Frost"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0006777.g003", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Contrasting_model_predictions_of_variation_in_recruitment_dynamics_/555401", "title"=>"Contrasting model predictions of variation in recruitment dynamics.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-09-07 01:30:01"}
{"files"=>["https://ndownloader.figshare.com/files/884876"], "description"=>"<p>Each node represents a respondent who recruits zero to three peers, indicated by arrows originating from the recruiter to the recruitee. The nodes are sized such that their diameter represents the respondent's reported network size on a logarithmic scale (base 10). A cross-mark inscribed within the node indicates that the respondent was not given coupons to recruit at the end of the study. Node shape indicates the interview site (circle = Sepanal/Zona Norte, square = Camino Verde). A doubled perimeter indicates that the respondent reported injecting drugs at home. Shaded nodes represent respondents who tested seropositive for syphilis.</p>", "links"=>[], "tags"=>["trees", "rds", "idus"], "article_id"=>555327, "categories"=>["Biological Sciences", "Medicine", "Biotechnology"], "users"=>["Art F. Y. Poon", "Kimberly C. Brouwer", "Steffanie A. Strathdee", "Michelle Firestone-Cruz", "Remedios M. Lozada", "Sergei L. Kosakovsky Pond", "Douglas D. Heckathorn", "Simon D. W. Frost"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0006777.g002", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Referral_trees_from_an_RDS_study_of_IDUs_in_Tijuana_M_233_xico_/555327", "title"=>"Referral trees from an RDS study of IDUs in Tijuana, México.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-09-07 01:28:47"}

PMC Usage Stats 129417 Mar 13:38 UTC

PMC Usage Stats | Further Information

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Relative Metric 182486310 May 16:14 UTC

Relative Metric

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F1000Prime07 Apr 11:01 UTC

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