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000839634 019__ $$a1039063522$$a1040614664
000839634 020__ $$a9783319899329$$q(electronic book)
000839634 020__ $$a3319899325$$q(electronic book)
000839634 020__ $$z3319899317
000839634 020__ $$z9783319899312
000839634 0247_ $$a10.1007/978-3-319-89932-9$$2doi
000839634 035__ $$aSP(OCoLC)on1038487285
000839634 035__ $$aSP(OCoLC)1038487285$$z(OCoLC)1039063522$$z(OCoLC)1040614664
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000839634 049__ $$aISEA
000839634 050_4 $$aHM742
000839634 08204 $$a302.23/1$$223
000839634 24500 $$aMachine learning techniques for online social networks /$$cTansel Özyer, Reda Alhajj, editors.
000839634 260__ $$aCham, Switzerland :$$bSpringer,$$c[2018]
000839634 300__ $$a1 online resource.
000839634 336__ $$atext$$btxt$$2rdacontent
000839634 337__ $$acomputer$$bc$$2rdamedia
000839634 338__ $$aonline resource$$bcr$$2rdacarrier
000839634 347__ $$atext file$$bPDF$$2rda
000839634 4901_ $$aLecture notes in social networks
000839634 504__ $$aIncludes bibliographical references.
000839634 5050_ $$aChapter1. Acceleration of Functional Cluster Extraction and Analysis of Cluster Affinity -- Chapter2. Delta-Hyperbolicity and the Core-Periphery Structure in Graphs -- Chapter3. A Framework for OSN Performance Evaluation Studies -- Chapter4. On The Problem of Multi-Staged Impression Allocation in Online Social Networks -- Chapter5. Order-of-Magnitude Popularity Estimation of Pirated Content -- Chapter6. Learning What to Share in Online Social Networks using Deep Reinforcement Learning -- Chapter7. Centrality and Community Scoring Functions in Incomplete Networks: Their Sensitivity, Robustness and Reliability -- Chapter8. Ameliorating Search Results Recommendation System based on K-means Clustering Algorithm and Distance Measurements -- Chapter9. Dynamics of large scale networks following a merger -- Chapter10. Cloud Assisted Personal Online Social Network -- Chapter11. Text-Based Analysis of Emotion by Considering Tweets.
000839634 506__ $$aAccess limited to authorized users.
000839634 520__ $$aThe book covers tools in the study of online social networks such as machine learning techniques, clustering, and deep learning. A variety of theoretical aspects, application domains, and case studies for analyzing social network data are covered. The aim is to provide new perspectives on utilizing machine learning and related scientific methods and techniques for social network analysis. Machine Learning Techniques for Online Social Networks will appeal to researchers and students in these fields.
000839634 588__ $$aOnline resource; title from PDF title page (viewed  June 13, 2018).
000839634 650_0 $$aOnline social networks$$xAnalysis.
000839634 650_0 $$aSocial media.
000839634 650_0 $$aMachine learning.
000839634 7001_ $$aÖzyer, Tansel,$$eeditor.
000839634 7001_ $$aAlhajj, Reda,$$eeditor.
000839634 77608 $$iPrint version:$$z3319899317$$z9783319899312$$w(OCoLC)1029205326
000839634 830_0 $$aLecture notes in social networks.
000839634 852__ $$bebk
000839634 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-89932-9$$zOnline Access$$91397441.1
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000839634 980__ $$aEBOOK
000839634 980__ $$aBIB
000839634 982__ $$aEbook
000839634 983__ $$aOnline
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