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Link Prediction in Social Networks Role of Power Law Distribution /
This work presents link prediction similarity measures for social networks that exploit the degree distribution of the networks. In the context of link prediction in dense networks, the text proposes similarity measures based on Markov inequality degree thresholding (MIDTs), which only consider node...
| Clasificación: | Libro Electrónico |
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| Autores principales: | , |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cham :
Springer International Publishing : Imprint: Springer,
2016.
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| Edición: | 1st ed. 2016. |
| Colección: | SpringerBriefs in Computer Science,
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| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 001 | 978-3-319-28922-9 | ||
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| 008 | 160122s2016 sz | s |||| 0|eng d | ||
| 020 | |a 9783319289229 |9 978-3-319-28922-9 | ||
| 024 | 7 | |a 10.1007/978-3-319-28922-9 |2 doi | |
| 050 | 4 | |a QA76.9.D343 | |
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| 082 | 0 | 4 | |a 006.312 |2 23 |
| 100 | 1 | |a Virinchi, Srinivas. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Link Prediction in Social Networks |h [electronic resource] : |b Role of Power Law Distribution / |c by Srinivas Virinchi, Pabitra Mitra. |
| 250 | |a 1st ed. 2016. | ||
| 264 | 1 | |a Cham : |b Springer International Publishing : |b Imprint: Springer, |c 2016. | |
| 300 | |a IX, 67 p. 5 illus. in color. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 490 | 1 | |a SpringerBriefs in Computer Science, |x 2191-5776 | |
| 505 | 0 | |a Introduction -- Link Prediction Using Degree Thresholding -- Locally Adaptive Link Prediction -- Two Phase Framework for Link Prediction -- Applications of Link Prediction -- Conclusion. | |
| 520 | |a This work presents link prediction similarity measures for social networks that exploit the degree distribution of the networks. In the context of link prediction in dense networks, the text proposes similarity measures based on Markov inequality degree thresholding (MIDTs), which only consider nodes whose degree is above a threshold for a possible link. Also presented are similarity measures based on cliques (CNC, AAC, RAC), which assign extra weight between nodes sharing a greater number of cliques. Additionally, a locally adaptive (LA) similarity measure is proposed that assigns different weights to common nodes based on the degree distribution of the local neighborhood and the degree distribution of the network. In the context of link prediction in dense networks, the text introduces a novel two-phase framework that adds edges to the sparse graph to forma boost graph. | ||
| 650 | 0 | |a Data mining. | |
| 650 | 0 | |a Computer networks . | |
| 650 | 1 | 4 | |a Data Mining and Knowledge Discovery. |
| 650 | 2 | 4 | |a Computer Communication Networks. |
| 700 | 1 | |a Mitra, Pabitra. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783319289212 |
| 776 | 0 | 8 | |i Printed edition: |z 9783319289236 |
| 830 | 0 | |a SpringerBriefs in Computer Science, |x 2191-5776 | |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-3-319-28922-9 |z Texto Completo |
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| 950 | |a Computer Science (SpringerNature-11645) | ||
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