Machine Learning Techniques for Online Social Networks

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...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Άλλοι συγγραφείς: Özyer, Tansel (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Alhajj, Reda (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2018.
Έκδοση:1st ed. 2018.
Σειρά:Lecture Notes in Social Networks,
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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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. 
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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. 
650 0 |a Social sciences-Computer programs. 
650 0 |a Data mining. 
650 0 |a Social media. 
650 0 |a Artificial intelligence. 
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650 2 4 |a Data Mining and Knowledge Discovery.  |0 http://scigraph.springernature.com/things/product-market-codes/I18030 
650 2 4 |a Social Media.  |0 http://scigraph.springernature.com/things/product-market-codes/412020 
650 2 4 |a Artificial Intelligence.  |0 http://scigraph.springernature.com/things/product-market-codes/I21000 
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700 1 |a Alhajj, Reda.  |e editor.  |4 edt  |4 http://id.loc.gov/vocabulary/relators/edt 
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