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 Ozyer, Reda Alhajj.
other author:	Ozyer, Tansel.
Published:	Cham :Springer International Publishing : : 2018.,
Description:	viii, 236 p. :digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	Online social networks - Analysis. -
Online resource:	http://dx.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 Ozyer, Reda Alhajj. - Cham :Springer International Publishing :2018. - viii, 236 p. :digital ;24 cm. - 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:

1205658
Online social networks
--Analysis.

LC Class. No.: HM742

Dewey Class. No.: 302.231

Machine learning techniques for online social networks
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300 $a viii, 236 p. : $b digital ; $c 24 cm.
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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.
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