000 | 02792nam a22005055i 4500 | ||
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001 | 978-3-319-28922-9 | ||
003 | DE-He213 | ||
005 | 20200421112226.0 | ||
007 | cr nn 008mamaa | ||
008 | 160122s2016 gw | s |||| 0|eng d | ||
020 |
_a9783319289229 _9978-3-319-28922-9 |
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024 | 7 |
_a10.1007/978-3-319-28922-9 _2doi |
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050 | 4 | _aQA76.9.D343 | |
072 | 7 |
_aUNF _2bicssc |
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072 | 7 |
_aUYQE _2bicssc |
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072 | 7 |
_aCOM021030 _2bisacsh |
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082 | 0 | 4 |
_a006.312 _223 |
100 | 1 |
_aSrinivas, Virinchi. _eauthor. |
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245 | 1 | 0 |
_aLink Prediction in Social Networks _h[electronic resource] : _bRole of Power Law Distribution / _cby Virinchi Srinivas, Pabitra Mitra. |
250 | _a1st ed. 2016. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2016. |
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300 |
_aIX, 67 p. 5 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aSpringerBriefs in Computer Science, _x2191-5768 |
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505 | 0 | _aIntroduction -- Link Prediction Using Degree Thresholding -- Locally Adaptive Link Prediction -- Two Phase Framework for Link Prediction -- Applications of Link Prediction -- Conclusion. | |
520 | _aThis 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 | _aComputer science. | |
650 | 0 | _aComputer communication systems. | |
650 | 0 | _aData mining. | |
650 | 1 | 4 | _aComputer Science. |
650 | 2 | 4 | _aData Mining and Knowledge Discovery. |
650 | 2 | 4 | _aComputer Communication Networks. |
700 | 1 |
_aMitra, Pabitra. _eauthor. |
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710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9783319289212 |
830 | 0 |
_aSpringerBriefs in Computer Science, _x2191-5768 |
|
856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/978-3-319-28922-9 |
912 | _aZDB-2-SCS | ||
942 | _cEBK | ||
999 |
_c57711 _d57711 |