Shadow networks: Discovering hidden nodes with models of information flow
Complex, dynamic networks underlie many systems, and understanding these networks is the concern of a great span of important scientific and engineering problems. Quantitative description is crucial for this understanding yet, due to a range of measurement problems, many real network datasets are in...
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| creator | Bagrow, James P Desu, Suma Frank, Morgan R Manukyan, Narine Mitchell, Lewis Reagan, Andrew Bloedorn, Eric E Booker, Lashon B Branting, Luther K Smith, Michael J Tivnan, Brian F Danforth, Christopher M Dodds, Peter S Bongard, Joshua C |
| description | Complex, dynamic networks underlie many systems, and understanding these
networks is the concern of a great span of important scientific and engineering
problems. Quantitative description is crucial for this understanding yet, due
to a range of measurement problems, many real network datasets are incomplete.
Here we explore how accidentally missing or deliberately hidden nodes may be
detected in networks by the effect of their absence on predictions of the speed
with which information flows through the network. We use Symbolic Regression
(SR) to learn models relating information flow to network topology. These
models show localized, systematic, and non-random discrepancies when applied to
test networks with intentionally masked nodes, demonstrating the ability to
detect the presence of missing nodes and where in the network those nodes are
likely to reside. |
| doi_str_mv | 10.48550/arxiv.1312.6122 |
| format | Article |
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networks is the concern of a great span of important scientific and engineering
problems. Quantitative description is crucial for this understanding yet, due
to a range of measurement problems, many real network datasets are incomplete.
Here we explore how accidentally missing or deliberately hidden nodes may be
detected in networks by the effect of their absence on predictions of the speed
with which information flows through the network. We use Symbolic Regression
(SR) to learn models relating information flow to network topology. These
models show localized, systematic, and non-random discrepancies when applied to
test networks with intentionally masked nodes, demonstrating the ability to
detect the presence of missing nodes and where in the network those nodes are
likely to reside.</description><identifier>DOI: 10.48550/arxiv.1312.6122</identifier><language>eng</language><subject>Computer Science - Social and Information Networks ; Physics - Data Analysis, Statistics and Probability ; Physics - Disordered Systems and Neural Networks ; Physics - Physics and Society</subject><creationdate>2013-12</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/1312.6122$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.1312.6122$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Bagrow, James P</creatorcontrib><creatorcontrib>Desu, Suma</creatorcontrib><creatorcontrib>Frank, Morgan R</creatorcontrib><creatorcontrib>Manukyan, Narine</creatorcontrib><creatorcontrib>Mitchell, Lewis</creatorcontrib><creatorcontrib>Reagan, Andrew</creatorcontrib><creatorcontrib>Bloedorn, Eric E</creatorcontrib><creatorcontrib>Booker, Lashon B</creatorcontrib><creatorcontrib>Branting, Luther K</creatorcontrib><creatorcontrib>Smith, Michael J</creatorcontrib><creatorcontrib>Tivnan, Brian F</creatorcontrib><creatorcontrib>Danforth, Christopher M</creatorcontrib><creatorcontrib>Dodds, Peter S</creatorcontrib><creatorcontrib>Bongard, Joshua C</creatorcontrib><title>Shadow networks: Discovering hidden nodes with models of information flow</title><description>Complex, dynamic networks underlie many systems, and understanding these
networks is the concern of a great span of important scientific and engineering
problems. Quantitative description is crucial for this understanding yet, due
to a range of measurement problems, many real network datasets are incomplete.
Here we explore how accidentally missing or deliberately hidden nodes may be
detected in networks by the effect of their absence on predictions of the speed
with which information flows through the network. We use Symbolic Regression
(SR) to learn models relating information flow to network topology. These
models show localized, systematic, and non-random discrepancies when applied to
test networks with intentionally masked nodes, demonstrating the ability to
detect the presence of missing nodes and where in the network those nodes are
likely to reside.</description><subject>Computer Science - Social and Information Networks</subject><subject>Physics - Data Analysis, Statistics and Probability</subject><subject>Physics - Disordered Systems and Neural Networks</subject><subject>Physics - Physics and Society</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotz71OwzAUhmEvDKiwMyHfQEJ8HP-xofJXqRID3SPHxyEWiY3sqIG7hwLT906f9BByxZq61UI0NzZ_hmPNOINaMoBzsnsdLaaVRr-sKb-XW3ofiktHn0N8o2NA9JHGhL7QNSwjnX9yKjQNNMQh5dkuIUU6TGm9IGeDnYq__N8NOTw-HLbP1f7labe921dWCqhaJ4yTIDlKaMCLXrWIRnEQwjTWoZJOYt_KRjOtlUbnEJ3pQRhlpATBN-T67_aX0n3kMNv81Z1I3YnEvwGbSEZl</recordid><startdate>20131220</startdate><enddate>20131220</enddate><creator>Bagrow, James P</creator><creator>Desu, Suma</creator><creator>Frank, Morgan R</creator><creator>Manukyan, Narine</creator><creator>Mitchell, Lewis</creator><creator>Reagan, Andrew</creator><creator>Bloedorn, Eric E</creator><creator>Booker, Lashon B</creator><creator>Branting, Luther K</creator><creator>Smith, Michael J</creator><creator>Tivnan, Brian F</creator><creator>Danforth, Christopher M</creator><creator>Dodds, Peter S</creator><creator>Bongard, Joshua C</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20131220</creationdate><title>Shadow networks: Discovering hidden nodes with models of information flow</title><author>Bagrow, James P ; Desu, Suma ; Frank, Morgan R ; Manukyan, Narine ; Mitchell, Lewis ; Reagan, Andrew ; Bloedorn, Eric E ; Booker, Lashon B ; Branting, Luther K ; Smith, Michael J ; Tivnan, Brian F ; Danforth, Christopher M ; Dodds, Peter S ; Bongard, Joshua C</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a652-4c59c6263d6202e5b74dd97325590acd76c6db460818878dccddc9b2597966253</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Computer Science - Social and Information Networks</topic><topic>Physics - Data Analysis, Statistics and Probability</topic><topic>Physics - Disordered Systems and Neural Networks</topic><topic>Physics - Physics and Society</topic><toplevel>online_resources</toplevel><creatorcontrib>Bagrow, James P</creatorcontrib><creatorcontrib>Desu, Suma</creatorcontrib><creatorcontrib>Frank, Morgan R</creatorcontrib><creatorcontrib>Manukyan, Narine</creatorcontrib><creatorcontrib>Mitchell, Lewis</creatorcontrib><creatorcontrib>Reagan, Andrew</creatorcontrib><creatorcontrib>Bloedorn, Eric E</creatorcontrib><creatorcontrib>Booker, Lashon B</creatorcontrib><creatorcontrib>Branting, Luther K</creatorcontrib><creatorcontrib>Smith, Michael J</creatorcontrib><creatorcontrib>Tivnan, Brian F</creatorcontrib><creatorcontrib>Danforth, Christopher M</creatorcontrib><creatorcontrib>Dodds, Peter S</creatorcontrib><creatorcontrib>Bongard, Joshua C</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Bagrow, James P</au><au>Desu, Suma</au><au>Frank, Morgan R</au><au>Manukyan, Narine</au><au>Mitchell, Lewis</au><au>Reagan, Andrew</au><au>Bloedorn, Eric E</au><au>Booker, Lashon B</au><au>Branting, Luther K</au><au>Smith, Michael J</au><au>Tivnan, Brian F</au><au>Danforth, Christopher M</au><au>Dodds, Peter S</au><au>Bongard, Joshua C</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Shadow networks: Discovering hidden nodes with models of information flow</atitle><date>2013-12-20</date><risdate>2013</risdate><abstract>Complex, dynamic networks underlie many systems, and understanding these
networks is the concern of a great span of important scientific and engineering
problems. Quantitative description is crucial for this understanding yet, due
to a range of measurement problems, many real network datasets are incomplete.
Here we explore how accidentally missing or deliberately hidden nodes may be
detected in networks by the effect of their absence on predictions of the speed
with which information flows through the network. We use Symbolic Regression
(SR) to learn models relating information flow to network topology. These
models show localized, systematic, and non-random discrepancies when applied to
test networks with intentionally masked nodes, demonstrating the ability to
detect the presence of missing nodes and where in the network those nodes are
likely to reside.</abstract><doi>10.48550/arxiv.1312.6122</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Social and Information Networks Physics - Data Analysis, Statistics and Probability Physics - Disordered Systems and Neural Networks Physics - Physics and Society |
| title | Shadow networks: Discovering hidden nodes with models of information flow |
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