Virinchi Srinivas (15 results)
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Published by Springer 2016-01, 2016
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Published by Springer, 2016
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Published by Springer, 2016
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Published by Springer-Verlag New York Inc, 2016
Series: Book 199 of 322 - SpringerBriefs in Computer Science
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Published by Springer, 2016
Series: Book 199 of 322 - SpringerBriefs in Computer Science
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Taschenbuch. Condition: Neu. Link Prediction in Social Networks | Role of Power Law Distribution | Srinivas Virinchi (u. a.) | Taschenbuch | SpringerBriefs in Computer Science | ix | Englisch | 2016 | Springer | EAN 9783319289212 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juer…gen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Language: English
Published by Springer, 2016
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Published by Springer, 2016
Series: Book 199 of 322 - SpringerBriefs in Computer Science
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Language: English
Published by Springer, 2016
Series: Book 199 of 322 - SpringerBriefs in Computer Science
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Published by Springer, 2016
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Language: English
Published by Springer, 2016
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Condition: New. Print on Demand pp. 67.
Language: English
Published by Springer, 2016
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Language: English
Published by Springer International Publishing, 2016
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Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents anaccessible explanation of the role of power law degree distribution in linkpredictionDescribes arange of link prediction algorithms in an easy-to-understand mannerDiscusses the impl…ementation of both the popularlink prediction algor.
Language: English
Published by Springer, Springer International Publishing Jan 2016, 2016
Series: Book 199 of 322 - SpringerBriefs in Computer Science
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Thiswork presents link prediction similarity measures for social networks that exploitthe degree distribution of the networks. In the context of link prediction indense networks, the text proposes similarity measures based on Markov ine…qualitydegree thresholding (MIDTs), which only consider nodes whose degree is above a thresholdfor a possible link. Also presented are similarity measures based on cliques(CNC, AAC, RAC), which assign extra weight between nodes sharing a greater numberof cliques. Additionally, a locally adaptive (LA) similarity measure isproposed that assigns different weights to common nodes based on the degreedistribution of the local neighborhood and the degree distribution of thenetwork. In the context of link prediction in dense networks, the textintroduces a novel two-phase framework that adds edges to the sparse graph toforma boost graph.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 80 pp. Englisch.
