Link Prediction in Social Networks (Record no. 57711)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02792nam a22005055i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-319-28922-9 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20200421112226.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 160122s2016 gw | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783319289229 |
| -- | 978-3-319-28922-9 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 006.312 |
| 100 1# - AUTHOR NAME | |
| Author | Srinivas, Virinchi. |
| 245 10 - TITLE STATEMENT | |
| Title | Link Prediction in Social Networks |
| Sub Title | Role of Power Law Distribution / |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2016. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | IX, 67 p. 5 illus. in color. |
| 490 1# - SERIES STATEMENT | |
| Series statement | SpringerBriefs in Computer Science, |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | Introduction -- Link Prediction Using Degree Thresholding -- Locally Adaptive Link Prediction -- Two Phase Framework for Link Prediction -- Applications of Link Prediction -- Conclusion. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | 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. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Mitra, Pabitra. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | http://dx.doi.org/10.1007/978-3-319-28922-9 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Cham : |
| -- | Springer International Publishing : |
| -- | Imprint: Springer, |
| -- | 2016. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
| -- | c |
| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
| -- | cr |
| -- | rdacarrier |
| 347 ## - | |
| -- | text file |
| -- | |
| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer science. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer communication systems. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Data mining. |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer Science. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Data Mining and Knowledge Discovery. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer Communication Networks. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| -- | 2191-5768 |
| 912 ## - | |
| -- | ZDB-2-SCS |
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