國立虎尾科技大學 |

Machine Learning Techniques for Online Social Networks

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Machine Learning Techniques for Online Social Networks/ edited by Tansel Özyer, Reda Alhajj.
other author:	Özyer, Tansel.
Description:	VIII, 236 p. 102 illus., 85 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Social sciences—Data processing. -
Online resource:	https://doi.org/10.1007/978-3-319-89932-9
ISBN:	9783319899329

Machine Learning Techniques for Online Social Networks
Machine Learning Techniques for Online Social Networks[electronic resource] /edited by Tansel Özyer, Reda Alhajj. - 1st ed. 2018. - VIII, 236 p. 102 illus., 85 illus. in color.online resource. - Lecture Notes in Social Networks,2190-5428. - Lecture Notes in Social Networks,.

Chapter1. Acceleration of Functional Cluster Extraction and Analysis of Cluster Affinity -- Chapter2. Delta-Hyperbolicity and the Core-Periphery Structure in Graphs -- Chapter3. A Framework for OSN Performance Evaluation Studies -- Chapter4. On The Problem of Multi-Staged Impression Allocation in Online Social Networks -- Chapter5. Order-of-Magnitude Popularity Estimation of Pirated Content -- Chapter6. Learning What to Share in Online Social Networks using Deep Reinforcement Learning -- Chapter7. Centrality and Community Scoring Functions in Incomplete Networks: Their Sensitivity, Robustness and Reliability -- Chapter8. Ameliorating Search Results Recommendation System based on K-means Clustering Algorithm and Distance Measurements -- Chapter9. Dynamics of large scale networks following a merger -- Chapter10. Cloud Assisted Personal Online Social Network -- Chapter11. Text-Based Analysis of Emotion by Considering Tweets.

The book covers tools in the study of online social networks such as machine learning techniques, clustering, and deep learning. A variety of theoretical aspects, application domains, and case studies for analyzing social network data are covered. The aim is to provide new perspectives on utilizing machine learning and related scientific methods and techniques for social network analysis. Machine Learning Techniques for Online Social Networks will appeal to researchers and students in these fields. .

ISBN: 9783319899329

Standard No.: 10.1007/978-3-319-89932-9doiSubjects--Topical Terms:

1280453
Social sciences—Data processing.

LC Class. No.: H61.3

Dewey Class. No.: 300.00285

Machine Learning Techniques for Online Social Networks
LDR:02859nam a22004095i 4500 001 994150
003 DE-He213
005 20200703165830.0
007 cr nn 008mamaa
008 201225s2018 gw | s |||| 0|eng d
020 $a 9783319899329 $9 978-3-319-89932-9
024 7 $a 10.1007/978-3-319-89932-9 $2 doi
035 $a 978-3-319-89932-9
050 4 $a H61.3
072 7 $a J $2 bicssc
072 7 $a SOC000000 $2 bisacsh
072 7 $a UXJ $2 thema
082 0 4 $a 300.00285 $2 23
245 1 0 $a Machine Learning Techniques for Online Social Networks $h [electronic resource] / $c edited by Tansel Özyer, Reda Alhajj.
250 $a 1st ed. 2018.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2018.
300 $a VIII, 236 p. 102 illus., 85 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 Lecture Notes in Social Networks, $x 2190-5428
505 0 $a Chapter1. Acceleration of Functional Cluster Extraction and Analysis of Cluster Affinity -- Chapter2. Delta-Hyperbolicity and the Core-Periphery Structure in Graphs -- Chapter3. A Framework for OSN Performance Evaluation Studies -- Chapter4. On The Problem of Multi-Staged Impression Allocation in Online Social Networks -- Chapter5. Order-of-Magnitude Popularity Estimation of Pirated Content -- Chapter6. Learning What to Share in Online Social Networks using Deep Reinforcement Learning -- Chapter7. Centrality and Community Scoring Functions in Incomplete Networks: Their Sensitivity, Robustness and Reliability -- Chapter8. Ameliorating Search Results Recommendation System based on K-means Clustering Algorithm and Distance Measurements -- Chapter9. Dynamics of large scale networks following a merger -- Chapter10. Cloud Assisted Personal Online Social Network -- Chapter11. Text-Based Analysis of Emotion by Considering Tweets.
520 $a The book covers tools in the study of online social networks such as machine learning techniques, clustering, and deep learning. A variety of theoretical aspects, application domains, and case studies for analyzing social network data are covered. The aim is to provide new perspectives on utilizing machine learning and related scientific methods and techniques for social network analysis. Machine Learning Techniques for Online Social Networks will appeal to researchers and students in these fields. .
650 0 $a Social sciences—Data processing. $3 1280453
650 0 $a Social sciences—Computer programs. $3 1280454
650 0 $a Data mining. $3 528622
650 0 $a Social media. $3 780265
650 0 $a Artificial intelligence. $3 559380
650 1 4 $a Computational Social Sciences. $3 1141127
650 2 4 $a Data Mining and Knowledge Discovery. $3 677765
650 2 4 $a Social Media. $3 1106917
650 2 4 $a Artificial Intelligence. $3 646849
700 1 $a Özyer, Tansel. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1285487
700 1 $a Alhajj, Reda. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1205657
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783319899312
776 0 8 $i Printed edition: $z 9783319899336
776 0 8 $i Printed edition: $z 9783030078966
830 0 $a Lecture Notes in Social Networks, $x 2190-5428 $3 1258143
856 4 0 $u https://doi.org/10.1007/978-3-319-89932-9
912 $a ZDB-2-SLS
912 $a ZDB-2-SXS
950 $a Social Sciences (SpringerNature-41176)
950 $a Social Sciences (R0) (SpringerNature-43726)