Language:
English
繁體中文
Help
Login
Back
Switch To:
Labeled
|
MARC Mode
|
ISBD
Link Prediction in Social Networks =...
~
Mitra, Pabitra.
Link Prediction in Social Networks = Role of Power Law Distribution /
Record Type:
Language materials, printed : Monograph/item
Title/Author:
Link Prediction in Social Networks/ by Srinivas Virinchi, Pabitra Mitra.
Reminder of title:
Role of Power Law Distribution /
Author:
Virinchi, Srinivas.
other author:
Mitra, Pabitra.
Description:
IX, 67 p. 5 illus. in color.online resource. :
Contained By:
Springer Nature eBook
Subject:
Data mining. -
Online resource:
https://doi.org/10.1007/978-3-319-28922-9
ISBN:
9783319289229
Link Prediction in Social Networks = Role of Power Law Distribution /
Virinchi, Srinivas.
Link Prediction in Social Networks
Role of Power Law Distribution /[electronic resource] :by Srinivas Virinchi, Pabitra Mitra. - 1st ed. 2016. - IX, 67 p. 5 illus. in color.online resource. - SpringerBriefs in Computer Science,2191-5768. - SpringerBriefs in Computer Science,.
Introduction -- Link Prediction Using Degree Thresholding -- Locally Adaptive Link Prediction -- Two Phase Framework for Link Prediction -- Applications of Link Prediction -- Conclusion.
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.
ISBN: 9783319289229
Standard No.: 10.1007/978-3-319-28922-9doiSubjects--Topical Terms:
528622
Data mining.
LC Class. No.: QA76.9.D343
Dewey Class. No.: 006.312
Link Prediction in Social Networks = Role of Power Law Distribution /
LDR
:02477nam a22004095i 4500
001
979295
003
DE-He213
005
20200704040527.0
007
cr nn 008mamaa
008
201211s2016 gw | 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
035
$a
978-3-319-28922-9
050
4
$a
QA76.9.D343
072
7
$a
UNF
$2
bicssc
072
7
$a
COM021030
$2
bisacsh
072
7
$a
UNF
$2
thema
072
7
$a
UYQE
$2
thema
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
$3
1272408
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
2191-5768
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.
$3
528622
650
0
$a
Computer communication systems.
$3
1115394
650
1 4
$a
Data Mining and Knowledge Discovery.
$3
677765
650
2 4
$a
Computer Communication Networks.
$3
669310
700
1
$a
Mitra, Pabitra.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1104658
710
2
$a
SpringerLink (Online service)
$3
593884
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
2191-5768
$3
1255334
856
4 0
$u
https://doi.org/10.1007/978-3-319-28922-9
912
$a
ZDB-2-SCS
912
$a
ZDB-2-SXCS
950
$a
Computer Science (SpringerNature-11645)
950
$a
Computer Science (R0) (SpringerNature-43710)
based on 0 review(s)
Multimedia
Reviews
Add a review
and share your thoughts with other readers
Export
pickup library
Processing
...
Change password
Login