000			02827nam a22004215i 4500
001			85522
003			ES-MaUEC
005			20230207040514.0
007			cr nn 008mamaa
008			160122s2016 gw | s |||| 0|eng d
020			_a9783319289229
040			_aES-MaUEC
050		4	_aQA76.9.D343 _bS656 2016 EB
082	0	4	_a006.312
100	1		_aSrinivas, Virinchi _998548 _0Local
245	1	0	_aLink Prediction in Social Networks : _bRole of Power Law Distribution _cby Virinchi Srinivas, Pabitra Mitra
250			_a1st ed.
260			_aCham _bSpringer International Publishing _c2016
300			_a1 recurso en línea (IX, 67 p.) _b5 ilustraciones en color
336			_aTexto (visual) _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	1		_aSpringerBriefs in Computer Science _x2191-5768
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
710	2		_aSpringerLink (Online service) _0Local _9106996
942			_2lcc _cLE
988			_aEBOOK, EBSPRINGER
650		7	_aData mining _0comprobar BNE20033218554 _2embne _9162648
650		7	_aRedes informáticas _0comprobar BNE19900997487 _2embne _9141354
700	1		_aMitra, Pabitra _eautor _998549 _0Local
830		0	_aSpringerBriefs in Computer Science _x2191-5768 _9134081
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-319-28922-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
901			_ai9783319289229
907			_a.b12948007 _b10-10-17 _c21-11-16
998			_am _a_alco _a_vill _b11-07-17 _cm _dz _ek _feng _ggw _h0
945			_aQA76.9.D343 S656 2016 EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i1159150x _z06-04-17
999			_c85522 _d85522 _x1