Muutke küpsiste eelistusi

Social Networks: Modelling and Analysis [Kõva köide]

(Jaypee Institute of Information Technology, India), (University of Delhi, Delhi, India)
  • Formaat: Hardback, 236 pages, kõrgus x laius: 234x156 mm, kaal: 494 g, 6 Tables, black and white; 202 Line drawings, black and white; 4 Halftones, black and white; 206 Illustrations, black and white
  • Sari: Advanced Research in Reliability and System Assurance Engineering
  • Ilmumisaeg: 18-Feb-2022
  • Kirjastus: CRC Press
  • ISBN-10: 0367541394
  • ISBN-13: 9780367541392
Teised raamatud teemal:
Social Networks: Modelling and AnalysisSuurem pilt
  • Formaat: Hardback, 236 pages, kõrgus x laius: 234x156 mm, kaal: 494 g, 6 Tables, black and white; 202 Line drawings, black and white; 4 Halftones, black and white; 206 Illustrations, black and white
  • Sari: Advanced Research in Reliability and System Assurance Engineering
  • Ilmumisaeg: 18-Feb-2022
  • Kirjastus: CRC Press
  • ISBN-10: 0367541394
  • ISBN-13: 9780367541392
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780367541392.html
Märksõnad:
The goal of this book is to provide a reference for applications of mathematical modelling in social media and related network analysis and offer a theoretically sound background with adequate suggestions for better decision-making.

Social Networks: Modelling and Analysis provides the essential knowledge of network analysis applicable to real-world data, with examples from today's most popular social networks such as Facebook, Twitter, Instagram, YouTube, etc. The book provides basic notation and terminology used in social media and its network science. It covers the analysis of statistics for social network analysis such as degree distribution, centrality, clustering coefficient, diameter, and path length. The ranking of the pages using rank algorithms such as Page Rank and HITS are also discussed.

Written as a reference this book is for engineering and management students, research scientists, as well as academicians involved in complex networks, mathematical sciences, and marketing research.
Preface xiii
Acknowledgements xv
Authors xvii
Chapter 1 Introduction to Social Networks
1(12)
1.1 Concept of Complex Networks
1(1)
1.2 Overview of Social Network Analysis
1(5)
1.2.1 Social Networks and Social Networking
1(2)
1.2.2 Social Network Visualization and Statistical Analysis
3(2)
1.2.3 Social Network Modelling
5(1)
1.2.4 Link Prediction
5(1)
1.2.5 Community Detection
5(1)
1.2.6 Ego Network
5(1)
1.2.7 Network Motifs
6(1)
1.2.8 Security and Privacy Issues
6(1)
1.3 Social Media Content
6(1)
1.3.1 Content Characteristics
6(1)
1.3.2 Content Dynamics
6(1)
1.3.3 User Characteristics
7(1)
1.4 Levels of Network Analysis
7(2)
1.4.1 Micro-Level
7(1)
1.4.2 Meso-Level
8(1)
1.4.3 Macro-Level
9(1)
1.5 Complex Networks
9(2)
1.6 Problems for Self-Assessment
11(2)
References
11(2)
Chapter 2 Network Statistics and Related Concepts
13(24)
2.1 Networks and Graphs
13(2)
2.2 Different Types of Networks
15(7)
2.2.1 Undirected Networks
15(1)
2.2.2 Directed Networks
16(4)
2.2.3 Self-Loops
20(1)
2.2.4 Multigraph/Simple Graphs
20(1)
2.2.5 Weighted Network
20(1)
2.2.6 Complete Graph (Clique)
20(1)
2.2.7 Bipartite Graph
21(1)
2.3 Representation of the Networks
22(6)
2.3.1 Adjacency Matrix
22(4)
2.3.2 Real Networks are Sparse
26(1)
2.3.3 Complete Graph
26(2)
2.4 Network Properties
28(7)
2.4.1 Node Degree
28(1)
2.4.2 Average Degree
28(1)
2.4.3 Degree Distribution
29(1)
2.4.4 Paths and Distance in Graph
30(2)
2.4.5 Shortest Path
32(1)
2.4.6 Network Diameter
32(1)
2.4.7 Average Path Length
32(1)
2.4.8 Clustering Coefficient
33(2)
2.5 Problems for Self-Assessment
35(2)
References
36(1)
Chapter 3 Network Models
37(16)
3.1 Basic Features of Networks
37(2)
3.1.1 Continuous Distribution
37(1)
3.1.2 Discrete Distribution
37(2)
3.2 Generative Models
39(11)
3.2.1 Random Graph Models
39(6)
3.2.2 Preferential Attachment Model
45(3)
3.2.3 Small-World Model
48(2)
3.3 Six Degrees of Separation
50(1)
3.4 Problems for Self-Assessment
51(2)
References
52(1)
Chapter 4 Network Centrality
53(14)
4.1 Centrality Measures Overview
53(1)
4.2 Degree Centrality
54(3)
4.3 Eigenvector Centrality
57(2)
4.4 Katz Centrality
59(1)
4.5 Betweenness Centrality
60(2)
4.6 Closeness Centrality
62(2)
4.7 Problems for Self-Assessment
64(3)
References
65(2)
Chapter 5 Link Analysis
67(16)
5.1 Link Analysis in Web Mining
67(1)
5.2 Ranking Algorithms
68(1)
5.3 Hyperlink-Induced Topic Search (HITS)
69(7)
5.4 Pagerank Algorithm
76(4)
5.5 Problems for Self-Assessment
80(3)
References
81(2)
Chapter 6 Link Prediction
83(14)
6.1 Overview of Link Prediction
83(1)
6.2 Link Prediction Methods
83(7)
6.2.1 Graph Distance
83(1)
6.2.2 Common Neighbours
84(1)
6.2.3 Jaccard's Coefficient
85(1)
6.2.4 Adamic/Adar (Frequency-Weighted Common Neighbours)
86(1)
6.2.5 Preferential Attachment
87(1)
6.2.6 Katz (Exponentially Damped Path Counts)
87(2)
6.2.7 Hitting Time
89(1)
6.2.8 Rooted (Personalized) PageRank
89(1)
6.3 Other Metrics
90(4)
6.3.1 Friends Measure
90(1)
6.3.2 Cosine Similarity
90(1)
6.3.3 Sorensen Index
91(1)
6.3.4 Hub Promoted Index
92(1)
6.3.5 Hub Depressed Index
93(1)
6.3.6 Leicht-Holme-Newman Index
93(1)
6.4 Prediction Performance Metrics
94(1)
6.5 Problems for Self-Assessment
95(2)
References
95(2)
Chapter 7 Community Detection
97(46)
7.1 Overview of Community
97(1)
7.2 Taxonomy of Community Criteria
98(38)
7.2.1 Node-Centric Community Detection
99(6)
7.2.2 Group-Centric Community Detection
105(1)
7.2.3 Network-Centric Community Detection
106(11)
7.2.4 Hierarchy-Centric Community Detection
117(19)
7.3 Community Evaluation
136(3)
7.4 Problems for Self-Assessment
139(4)
References
141(2)
Chapter 8 Ego Networks
143(12)
8.1 Overview of Ego Networks
143(1)
8.2 Characteristics of Ego Networks
144(1)
8.3 Ego Network Measures
145(8)
8.3.1 Ego Network Density
145(1)
8.3.2 Structural Holes
145(6)
8.3.3 Brokerage
151(2)
8.4 Problems for Self-Assessment
153(2)
References
153(2)
Chapter 9 Network Cohesion
155(18)
9.1 Overview of Network Cohesion
155(1)
9.2 Triadic Closure
155(1)
9.3 Embeddedness
156(1)
9.4 Density
157(1)
9.5 Dyadic Relation
157(1)
9.6 Reciprocity
158(2)
9.7 Homophily
160(1)
9.8 Transitivity
161(2)
9.9 Bridges
163(1)
9.10 Group-External and Group-Internal Ties
164(2)
9.11 Krackhardt's Graph Theoretical Dimensions of Hierarchy
166(1)
9.12 Positions and Roles
167(3)
9.13 Problems for Self-Assessment
170(3)
References
170(3)
Chapter 10 Information Diffusion
173(18)
10.1 Overview of Information Diffusion
173(1)
10.2 Explicit Networks
174(6)
10.2.1 Herd Behaviour
175(1)
10.2.2 Information Cascades
176(4)
10.3 Implicit Networks
180(8)
10.3.1 Diffusion of Innovations
180(5)
10.3.2 Epidemical Models
185(3)
10.4 Problems for Self-Assessment
188(3)
References
189(2)
Chapter 11 Security and Privacy in Social Networks
191(14)
11.1 Introduction
191(2)
11.2 Need of Privacy
193(1)
11.3 Social Network Privacy Model
194(1)
11.4 Basic Concepts in Data Privacy
195(6)
11.4.1 K-Anonymity
195(2)
11.4.2 L-Diversity
197(3)
11.4.3 T-Closeness
200(1)
11.5 Randomization
201(1)
11.6 Slicing
201(2)
11.7 Problems for Self-Assessment
203(2)
References
204(1)
Chapter 12 Social Network Analysis Tools
205(28)
12.1 Overview of Social Network Analysis Tools
205(1)
12.2 Various Tools
205(6)
12.2.1 Gephi (Visualization and Basic Network Metrics)
205(1)
12.2.2 NetLogo (Modelling Network Dynamics)
206(1)
12.2.3 Igraph (for Programming Assignment)
207(1)
12.2.4 Pajek (User Friendly, Free, Windows Only)
207(1)
12.2.5 UCINET (Extensive, Socially Focused Functionality, Windows Only)
208(1)
12.2.6 Network Overview Discovery Exploration for Excel (NodeXL) (SNA Integrated to Excel, Windows Only, Free, Beta)
208(1)
12.2.7 NetMiner4
209(1)
12.2.8 NetworkX (Extensive Functionality, Scales to Large Networks by Taking Advantage of Existing C, Fortran Libraries for Large Matrix Computations, Open Source)
209(1)
12.2.9 R (Extensive, Statistics-Heavy Functionality)
210(1)
12.2.10 SocioViz
210(1)
12.2.11 UNISoN (Social Network Analysis Tool)
211(1)
12.2.12 Wolfram Alpha
211(1)
12.3 Problems for Self-Assessment
211(22)
References
232(1)
Index 233
Dr. Niyati Aggrawal is an accomplished and integrity-driven academic professional with over 10 years of rich experience in varied fields of social network analysis, social media mining, information diffusion on social media, web mining techniques and information security. She has guided various graduate projects in the fields of social network analysis and data mining. She has published several research papers in many reputed international journals and conferences related to the above- mentioned research areas. She has actively participated in various conferences, faculty develop-ment programs and workshops.

Dr. Adarsh Anand completed his doctorate in the area of Innovation Diffusion Modelling in Marketing and Software Reliability Assessment. Presently, he is working as an assistant professor in the Department of Operational Research, University of Delhi (India). In 2012, he was conferred with the Young Promising Researcher in the field of Technology Management and Software Reliability by Society for Reliability Engineering, Quality and Operations Management (SREQOM). He is a lifetime member of SREQOM. He is also on the editorial board of the International Journal of System Assurance and Engineering Management (Springer). He has guest edited several special issues for journals of international repute. He has edited two books, System Reliability Management (Solutions and Technologies) and Recent Advancements in Software Reliability Assurance, under the banner of Taylor and Francis (CRC Press). He has publications in journals of national and international repute. His research interests include modelling innovation adoption and successive generations in marketing, software reliability growth modelling and social media analysis.