000780320 000__ 05256cam\a2200565Ii\4500
000780320 001__ 780320
000780320 005__ 20230306143002.0
000780320 006__ m\\\\\o\\d\\\\\\\\
000780320 007__ cr\nn\nnnunnun
000780320 008__ 170321s2017\\\\sz\\\\\\o\\\\\000\0\eng\d
000780320 019__ $$a978654320$$a978860003$$a979244133$$a979408757$$a979761032$$a980216225$$a980457491$$a980637557$$a984872889
000780320 020__ $$a9783319510491$$q(electronic book)
000780320 020__ $$a3319510495$$q(electronic book)
000780320 020__ $$z9783319510484
000780320 020__ $$z3319510487
000780320 0247_ $$a10.1007/978-3-319-51049-1$$2doi
000780320 035__ $$aSP(OCoLC)ocn978248695
000780320 035__ $$aSP(OCoLC)978248695$$z(OCoLC)978654320$$z(OCoLC)978860003$$z(OCoLC)979244133$$z(OCoLC)979408757$$z(OCoLC)979761032$$z(OCoLC)980216225$$z(OCoLC)980457491$$z(OCoLC)980637557$$z(OCoLC)984872889
000780320 040__ $$aN$T$$beng$$erda$$epn$$cN$T$$dN$T$$dEBLCP$$dGW5XE$$dYDX$$dOCLCF$$dUAB$$dNJR$$dCCO$$dIOG$$dCOO$$dAZU$$dUPM
000780320 049__ $$aISEA
000780320 050_4 $$aHM901
000780320 066__ $$c(S
000780320 08204 $$a303.49$$223
000780320 24500 $$aPrediction and inference from social networks and social media /$$cJalal Kawash, Nitin Agarwal, Tansel Özyer, editor.
000780320 264_1 $$aCham :$$bSpringer,$$c2017.
000780320 300__ $$a1 online resource.
000780320 336__ $$atext$$btxt$$2rdacontent
000780320 337__ $$acomputer$$bc$$2rdamedia
000780320 338__ $$aonline resource$$bcr$$2rdacarrier
000780320 347__ $$atext file$$bPDF$$2rda
000780320 4901_ $$aLecture notes in social networks
000780320 5050_ $$aPreface; Contents; 1 Having Fun?: Personalized Activity-Based Mood Prediction in Social Media; 1 Introduction; 2 Related Work; 3 Social Media Data; 3.1 Twitter Dataset; 3.2 Ground Truth; 4 Features; 5 Prediction; 5.1 Prediction Framework; 5.2 General Prediction Results; 5.3 Personalized Prediction Results; 6 Conclusion and Future Work; References; 2 Automatic Medical Image Multilingual Indexation Through a Medical Social Network; 1 Introduction; 2 Related Work; 2.1 Medical Social Networks; 2.2 Multilingual Indexation Approaches; 2.2.1 An Overview
000780320 5058_ $$a2.2.2 Indexation Approaches via Social Networks3 Social Network Architecture Description and Implementation; 4 The Proposed Methodology; 4.1 Comments' Pre-processing; 4.2 Cleaning, Correcting, and Lemmatization; 4.2.1 Cleaning; 4.2.2 Correcting Words; 4.2.3 Lemmatization Words; 4.3 Terms' Extraction; 4.3.1 Simple Terms' Extraction; 4.3.2 Compound Terms' Extraction; 4.3.3 Concepts' Extraction; 5 Experimental Results; 5.1 Data Test and Evaluation Criteria; 5.2 Evaluation and Results of Our Approach; 6 Conclusion and Future Work; References
000780320 5058_ $$a3 The Significant Effect of Overlapping Community Structures in Signed Social Networks1 Introduction; 1.1 Contribution of the Paper; 2 Related Work; 3 Use of Terms, Variables and Definitions; 4 Signed Disassortative Degree Mixing and Information Diffusion Approach; 4.1 Identifying Leaders; 4.2 Signed Cascading Process; 4.3 Overlapping Community-Based Ranking Algorithms; 4.3.1 Overlapping Community-Based HITS; 4.3.2 Overlapping Community-Based PageRank; 4.4 Baseline OCD Methods; 4.4.1 Signed Probabilistic Mixture Model ; 4.4.2 Multi-Objective Evolutionary Algorithm in Signed Networks
000780320 5058_ $$a5 Sign Prediction5.1 Classifiers; 5.1.1 Logistic Regression; 5.1.2 Bagging; 5.1.3 J48; 5.1.4 Decision Table; 5.1.5 Bayesian Network and Naive Bayesian; 5.2 Sign Prediction Features; 5.2.1 Simple Degree Sign Prediction Features; 5.2.2 OC-HITS Sign Prediction; 5.2.3 OC-PageRank Sign Prediction; 6 Dataset and Metrics; 6.1 Real World Networks; 6.2 Synthetic Networks; 6.3 Evaluation Metrics; 6.3.1 Normalized Mutual Information; 6.3.2 Modularity; 6.3.3 Frustration; 7 Results; 7.1 Results of OCD; 7.1.1 Network Size n; 7.1.2 Average Node Degree k; 7.1.3 Maximum Node Degree maxk
000780320 506__ $$aAccess limited to authorized users.
000780320 520__ $$aThis book addresses the challenges of social network and social media analysis in terms of prediction and inference. The chapters collected here tackle these issues by proposing new analysis methods and by examining mining methods for the vast amount of social content produced. Social Networks (SNs) have become an integral part of our lives; they are used for leisure, business, government, medical, educational purposes and have attracted billions of users. The challenges that stem from this wide adoption of SNs are vast. These include generating realistic social network topologies, awareness of user activities, topic and trend generation, estimation of user attributes from their social content, and behavior detection. This text has applications to widely used platforms such as Twitter and Facebook and appeals to students, researchers, and professionals in the field.
000780320 588__ $$aOnline resource; title from PDF title page (viewed March 24, 2017).
000780320 650_0 $$aSocial prediction.
000780320 650_0 $$aSocial networks.
000780320 650_0 $$aSocial media.
000780320 7001_ $$aKawash, Jalal,$$eeditor.
000780320 7001_ $$aAgarwal, Nitin,$$eeditor.
000780320 7001_ $$aÖzyer, Tansel,$$eeditor.
000780320 77608 $$iPrint version:$$z3319510487$$z9783319510484$$w(OCoLC)964291495
000780320 830_0 $$aLecture notes in social networks.
000780320 852__ $$bebk
000780320 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-51049-1$$zOnline Access$$91397441.1
000780320 909CO $$ooai:library.usi.edu:780320$$pGLOBAL_SET
000780320 980__ $$aEBOOK
000780320 980__ $$aBIB
000780320 982__ $$aEbook
000780320 983__ $$aOnline
000780320 994__ $$a92$$bISE