LEADER 01876nam0 2200325 i 450 001 VAN00030549 005 20240806100344.267 010 $a978-05-214-2725-8 100 $a20041215d1994 |0itac50 ba 101 $aeng 102 $aGB 105 $a|||| ||||| 200 1 $aArithmetical functions$ean introduction to elementary and analytical properties of arithmetic functions and to some of their almost-periodic properties$fWolfgang Schwarz, Jurgen Spilker 210 $aCambridge$cCambridge university$d1994 215 $aXIX, 367 p.$d22 cm 410 1$1001VAN00029528$12001 $aLondon Mathematical Society lecture notes series$1210 $aCambridge$cCambridge university$v184 606 $a11-XX$xNumber theory [MSC 2020]$3VANC019688$2MF 606 $a11K65$xArithmetic functions in probabilistic number theory [MSC 2020]$3VANC021811$2MF 606 $a11N37$xAsymptotic results on arithmetic functions [MSC 2020]$3VANC021729$2MF 620 $dCambridge$3VANL000024 700 1$aSchwarz$bWolfgang K.$f1934-2013$3VANV025219$01740098 701 1$aSpilker$bJürgen$3VANV025220$0728476 712 $aCambridge University $3VANV107986$4650 801 $aIT$bSOL$c20250124$gRICA 856 4 $uhttps://books.google.it/books?id=myiP_n4camIC&pg=PA97&dq=9780521427258&hl=it&sa=X&ved=0ahUKEwjHiZmVwdfaAhWDzRQKHamoDo8QuwUILjAA#v=onepage&q=9780521427258&f=false$zhttps://books.google.it/books?id=myiP_n4camIC&pg=PA97&dq=9780521427258&hl=it&sa=X&ved=0ahUKEwjHiZmVwdfaAhWDzRQKHamoDo8QuwUILjAA#v=onepage&q=9780521427258&f=false 899 $aBIBLIOTECA DEL DIPARTIMENTO DI MATEMATICA E FISICA$1IT-CE0120$2VAN08 912 $aVAN00030549 950 $aBIBLIOTECA DEL DIPARTIMENTO DI MATEMATICA E FISICA$d08PREST 11-XX 3950 $e08 2590 I 20041215 996 $aArithmetical functions$94165379 997 $aUNICAMPANIA LEADER 06133nam 22004933 450 001 9910985641303321 005 20240710080302.0 010 $a9781779561169 010 $a1779561164 035 $a(MiAaPQ)EBC31520879 035 $a(Au-PeEL)EBL31520879 035 $a(CKB)32691701900041 035 $a(Exl-AI)31520879 035 $a(OCoLC)1446129737 035 $a(EXLCZ)9932691701900041 100 $a20240710d2024 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aComputational Models for Social Network Analysis 205 $a1st ed. 210 1$aBurlington :$cArcler Education Inc,$d2024. 210 4$d©2024. 215 $a1 online resource (401 pages) 311 08$a9781774699126 311 08$a1774699125 327 $aCover -- Half Title -- Title Page -- Copyright -- About The Author -- Table of Contents -- List of Figures -- List of Abbreviations -- Preface -- Chapter 1: Introduction To Social Network Analysis -- Contents -- 1.1. Theoretical Foundations Of Social Network Analysis -- 1.2. Key Concepts In Social Network Analysis -- 1.3. Importance Of Computational Models In Social Network Analysis -- 1.4. Challenges And Limitations Of Social Network Analysis -- 1.5. Ethical Considerations In Social Network Analysis -- Summary -- Chapter 1 Review Questions -- Chapter 2: Network Data Collection And Representation -- Contents -- 2.1. Data Collection Methods For Social Network Analysis -- 2.2. Network Data Types And Formats -- 2.3. Data Preprocessing And Cleaning For Social Network Analysis -- 2.4. Visualization Techniques For Network Data -- 2.5. Network Metrics And Descriptive Statistics -- Chapter 2 Summary -- Chapter 2 Review Questions -- Chapter 3: Centrality And Influence Measures -- Contents -- 3.1. Degree Centrality And Its Applications -- 3.2. Betweenness Centrality And Its Significance -- 3.3. Closeness Centrality And Its Implications -- 3.4. Eigenvector Centrality And Its Role In Identifying Influencers -- 3.5. Pagerank Algorithm And Its Application To Social Networks -- Chapter 3 Summary -- Chapter 3 Review Questions -- Chapter 4: Community Detection And Analysis -- Contents -- 4.1. Overview Of Community Detection In Social Networks -- References -- 4.2. Modularity-based Community Detection Methods -- 4.3. Hierarchical Clustering Approaches For Community Detection -- 4.4. Spectral Clustering Techniques In Community Detection -- References -- 4.5. Evaluation Measures For Community Detection Algorithms -- References -- Chapter 4 Summary -- Chapter 4 Review Questions -- Chapter 5: Diffusion And Information Spread In Social Networks -- Contents. 327 $a5.1. Information Diffusion Models In Social Networks -- 5.2. Epidemic Models For Information Spread -- References -- 5.3. Influence Maximization And Viral Marketing Strategies -- References -- 5.4. Cascading Behavior And Contagion Dynamics -- References -- 5.5. Modeling And Analyzing Rumor Propagation In Social Networks -- References -- Chapter 5 Summary -- Chapter 5 Review Questions -- Chapter 6: Opinion Mining And Sentiment Analysis -- Contents -- 6.1. Sentiment Analysis Techniques For Social Network Data -- References -- 6.2. Opinion Mining In Social Media -- References -- 6.3. Aspect-based Sentiment Analysis In Social Networks -- References -- 6.4. Emotion Detection And Analysis In Online Social Interactions -- References -- 6.5. Sentiment Classification And Prediction Models -- References -- Chapter 6 Summary -- Chapter 6 Review Questions -- Chapter 7: Link Prediction And Recommender Systems -- Contents -- 7.1. Link Prediction Techniques In Social Networks -- References -- 7.2. Collaborative Filtering For Recommender Systems -- References -- 7.3. Content-based Filtering Methods For Recommender Systems -- References -- 7.4. Hybrid Approaches For Link Prediction And Recommender Systems -- References -- 7.5. Evaluation Metrics For Link Prediction And Recommender Systems -- References -- Chapter 7 Summary -- Chapter 7 Review Questions -- Chapter 8: Social Network Simulation And Modeling -- Contents -- 8.1. Agent-based Models For Social Networks -- References -- 8.2. Random Graph Models For Social Network Generation -- References -- 8.3. Dynamic Network Models And Temporal Analysis -- References -- 8.4. Simulation Of Social Influence And Behavior Diffusion -- References -- 8.5. Validation And Calibration Of Social Network Models -- References -- Chapter 8 Summary -- Chapter 8 Review Questions -- Concluding Remarks. 327 $aSummative Test Questions And Answers -- Bibliography -- Index -- Back Cover. 330 $aThis book, 'Models for Social Network Analysis' by Dr. Ravi Ranjan Kumar, provides a comprehensive guide to understanding computational models used in social network analysis. It covers fundamental concepts, methods, and applications, offering insights into network data collection, centrality and influence measures, community detection, diffusion of information, opinion mining, link prediction, recommender systems, and social network simulation. Aimed at undergraduate students, policymakers, and practitioners in marketing, public health, and social media, the book equips readers with the knowledge to analyze social networks computationally. It highlights the importance of computational models in informing decisions, designing strategies, and addressing societal challenges. It also discusses ethical considerations such as privacy and data protection. This work serves as an introduction to the theories and practical applications of social network analysis and encourages further exploration of the field.$7Generated by AI. 606 $aSocial networks$7Generated by AI 606 $aModeling$7Generated by AI 615 0$aSocial networks 615 0$aModeling 700 $aKumar$b Ravi Ranjan$01792531 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910985641303321 996 $aComputational Models for Social Network Analysis$94331202 997 $aUNINA