Muutke küpsiste eelistusi

E-raamat: Graph-Based Social Media Analysis

3.50/5 (4 hinnangut Goodreads-ist)
Edited by (Aristotle University of Thessaloniki, Greece)
Teised raamatud teemal:
  • Formaat - PDF+DRM
  • Hind: 55,89 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
Graph-Based Social Media AnalysisSuurem pilt
Teised raamatud teemal:

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

Focused on the mathematical foundations of social media analysis, Graph-Based Social Media Analysis provides a comprehensive introduction to the use of graph analysis in the study of social and digital media. It addresses an important scientific and technological challenge, namely the confluence of graph analysis and network theory with linear algebra, digital media, machine learning, big data analysis, and signal processing.

Supplying an overview of graph-based social media analysis, the book provides readers with a clear understanding of social media structure. It uses graph theory, particularly the algebraic description and analysis of graphs, in social media studies.

The book emphasizes the big data aspects of social and digital media. It presents various approaches to storing vast amounts of data online and retrieving that data in real-time. It demystifies complex social media phenomena, such as information diffusion, marketing and recommendation systems in social media, and evolving systems. It also covers emerging trends, such as big data analysis and social media evolution.

Describing how to conduct proper analysis of the social and digital media markets, the book provides insights into processing, storing, and visualizing big social media data and social graphs. It includes coverage of graphs in social and digital media, graph and hyper-graph fundamentals, mathematical foundations coming from linear algebra, algebraic graph analysis, graph clustering, community detection, graph matching, web search based on ranking, label propagation and diffusion in social media, graph-based pattern recognition and machine learning, graph-based pattern classification and dimensionality reduction, and much more.

This book is an ideal reference for scientists and engineers working in social media and digital media production and distribution. It is also suitable for use as a textbook in undergraduate or graduate courses on digital media, social media, or social networks.
Preface xiii
Contributors xv
Editor Biography xvii
1 Graphs in Social and Digital Media
1(20)
Alexandros Iosifidis
Nikolaos Tsapanos
Ioannis Pitas
1.1 Introduction
1(2)
1.2 Dominant social networking/media platforms
3(2)
1.3 Collecting data from social media sites
5(3)
1.4 Social media graphs
8(6)
1.4.1 Graphs from Facebook data
8(2)
1.4.2 Graphs from Twitter data
10(2)
1.4.3 Graphs from bibliographic data
12(2)
1.5 Graph storage formats and visualization
14(1)
1.6 Big data issues in social and digital media
15(1)
1.7 Distributed computing platforms
15(3)
1.8 Conclusions
18(3)
Bibliography
18(3)
2 Mathematical Preliminaries: Graphs and Matrices
21(14)
Nikolaos Tsapanos
Alexandros Iosifidis
Ioannis Pitas
2.1 Graph basics
21(3)
2.2 Linear algebra tools
24(4)
2.3 Matrix decompositions
28(3)
2.4 Vector and matrix derivatives
31(4)
Bibliography
31(4)
3 Algebraic Graph Analysis
35(32)
Nikolaos Tsapanos
Anastasios Tefas
Ioannis Pitas
3.1 Introduction
35(1)
3.2 Spectral graph theory
36(2)
3.2.1 Adjacency and Laplacian matrix
36(1)
3.2.2 Similarity matrix and nearest neighbor graph
37(1)
3.3 Applications of graph analysis
38(2)
3.4 Random graph generation
40(5)
3.4.1 Desirable random graph properties
41(1)
3.4.2 Random graph generation models
41(2)
3.4.3 Spectral graph generation
43(2)
3.5 Graph clustering
45(6)
3.5.1 Global clustering algorithms
46(2)
3.5.2 Local clustering algorithms
48(1)
3.5.3 Spectral clustering algorithms
48(2)
3.5.4 Overlapping community detection
50(1)
3.6 Graph matching
51(3)
3.6.1 Spectral graph matching
53(1)
3.6.2 Frequent subgraph mining
54(1)
3.7 Random walks
54(2)
3.8 Graph anomaly detection
56(2)
3.8.1 Spectral anomaly detection
57(1)
3.9 Conclusions
58(9)
Bibliography
59(8)
4 Web Search Based on Ranking
67(40)
Andrea Tagarelli
Santosh Kabbur
George Karypis
4.1 Introduction
67(2)
4.2 Information Retrieval Background
69(3)
4.2.1 Document representation
69(2)
4.2.2 Retrieval models
71(1)
4.3 Relevance Beyond the Web Page Text
72(4)
4.3.1 Anchor text
72(1)
4.3.2 Query expansion
73(3)
4.4 Centrality and Prestige
76(12)
4.4.1 Basic measures
77(3)
4.4.2 Eigenvector centrality and prestige
80(1)
4.4.3 PageRank
81(3)
4.4.4 Hubs and authorities
84(3)
4.4.5 SimRank
87(1)
4.5 Topic-Sensitive Ranking
88(4)
4.5.1 Content as topic
89(2)
4.5.2 Trust as topic
91(1)
4.6 Ranking in Heterogeneous Networks
92(5)
4.6.1 Ranking in heterogeneous information networks
93(2)
4.6.2 Ranking-Based clustering
95(2)
4.7 Organizing Search Results
97(2)
4.8 Conclusion
99(8)
Bibliography
100(7)
5 Label Propagation and Information Diffusion in Graphs
107(56)
Eftychia Fotiadou
Olga Zoidi
Ioannis Pitas
5.1 Introduction
108(1)
5.2 Graph construction approaches
109(11)
5.2.1 Neighborhood approaches
110(1)
5.2.2 Local reconstruction approaches
111(2)
5.2.3 Metric learning approaches
113(5)
5.2.4 Scalable graph construction methods
118(2)
5.3 Label inference methods
120(14)
5.3.1 Iterative algorithms
120(2)
5.3.2 Random walks
122(1)
5.3.3 Graph regularization
123(4)
5.3.4 Graph kernel regularization
127(1)
5.3.5 Inductive label inference
128(1)
5.3.6 Label propagation on data with multiple representations
129(2)
5.3.7 Label propagation on hypergraphs
131(1)
5.3.8 Label propagation initialization
132(1)
5.3.9 Applications in digital media
133(1)
5.4 Diffusion processes
134(2)
5.4.1 Diffusion in physics
134(1)
5.4.2 Diffusion in sociology
135(1)
5.4.3 Diffusion in social media
135(1)
5.5 Social network diffusion models
136(9)
5.5.1 Game theoretical diffusion models
137(1)
5.5.2 Epidemic diffusion models
137(1)
5.5.3 Threshold diffusion models
138(1)
5.5.4 Cascade diffusion models
139(1)
5.5.5 Influence maximization
140(2)
5.5.6 Cross-Media information diffusion
142(1)
5.5.7 Other applications of information diffusion
143(2)
5.6 Conclusions
145(18)
Bibliography
146(17)
6 Graph-Based Pattern Classification and Dimensionality Reduction
163(24)
Alexandros Iosifidis
Ioannis Pitas
6.1 Introduction
163(1)
6.2 Notations
164(2)
6.3 Unsupervised Methods
166(3)
6.3.1 Locality Preserving Projections
166(1)
6.3.2 Locally Linear Embedding
167(1)
6.3.3 ISOMAP
168(1)
6.3.4 Laplacian Embedding
168(1)
6.3.5 Diffusion Maps
168(1)
6.4 Supervised Methods
169(6)
6.4.1 Linear Discriminant Analysis
169(2)
6.4.2 Marginal Fisher Analysis
171(1)
6.4.3 Local Fisher Discriminant Analysis
171(1)
6.4.4 Graph Embedding
172(1)
6.4.5 Minimum Class Variance Extreme Learning Machine
173(1)
6.4.6 Minimum Class Variance Support Vector Machine
174(1)
6.4.7 Graph Embedded Support Vector Machines
174(1)
6.5 Semi-Supervised Methods
175(2)
6.5.1 Semi-Supervised Discriminant Analysis
176(1)
6.5.2 Laplacian Support Vector Machine
176(1)
6.5.3 Semi-Supervised Extreme Learning Machine
177(1)
6.6 Applications
177(1)
6.7 Conclusions
178(9)
Bibliography
179(8)
7 Matrix and Tensor Factorization with Recommender System Applications
187(28)
Panagiotis Symeonidis
7.1 Introduction
187(2)
7.2 Singular Value Decomposition on Matrices for Recommender Systems
189(4)
7.2.1 Applying the SVD and Preserving the Largest Singular Values
190(1)
7.2.2 Generating the Neighborhood of Users/Items
191(1)
7.2.3 Generating the Recommendation List
191(1)
7.2.4 Inserting a Test User in the c-dimensional Space
192(1)
7.2.5 Other Factorization Methods
192(1)
7.3 Higher Order Singular Value Decomposition (HOSVD) on Tensors
193(12)
7.3.1 From SVD to HOSVD
193(3)
7.3.2 HOSVD for Recommendations in Social Tagging Systems
196(4)
7.3.3 Handling the Sparsity Problem
200(1)
7.3.4 Inserting New Users, Tags, or Items
201(3)
7.3.5 Other Scalable Factorization Models
204(1)
7.4 A Real Geo-Social System-Based on HOSVD
205(5)
7.4.1 GeoSocialRec Website
205(2)
7.4.2 GeoSocialRec Database and Recommendation Engine
207(1)
7.4.3 Experiments
208(2)
7.5 Conclusion
210(5)
Bibliography
210(5)
8 Multimedia Social Search Based on Hypergraph Learning
215(60)
Constantine Kotropoulos
8.1 Introduction
215(3)
8.2 Hypergraphs
218(5)
8.2.1 Uniform hypergraphs
220(3)
8.3 Game-Theoretic approaches to uniform hypergraph clustering
223(6)
8.4 Spectral clustering for arbitrary hypergraphs
229(9)
8.5 Ranking on hypergraphs
238(5)
8.5.1 Enforcing structural constraints
239(2)
8.5.2 Learning hyperedge weights
241(2)
8.6 Applications
243(18)
8.6.1 High-order web link analysis
243(4)
8.6.2 Hypergraph matching for object recognition
247(2)
8.6.3 Music recommendation and personalized music tagging
249(2)
8.6.4 Simultaneous image tagging and geo-location prediction
251(3)
8.6.5 Social image search exploiting joint visual-textual information
254(2)
8.6.6 Annotation, classification, and tourism recommendation driven by probabilistic latent semantic analysis
256(5)
8.7 Big data: Randomized methods for matrix/hypermatrix decompositions
261(4)
8.8 Conclusions
265(2)
8.9 Acknowledgments
267(8)
Bibliography
267(8)
9 Graph Signal Processing in Social Media
275(18)
Sunil Narang
9.1 Motivation
275(2)
9.2 Graph signal processing (GSP)
277(5)
9.2.1 Basics of graph signal processing
277(2)
9.2.2 Spectral representation of graph signals
279(1)
9.2.3 Downsampling in graphs
280(2)
9.2.4 Graph wavelets and filterbanks
282(1)
9.3 Applications
282(7)
9.3.1 Information diffusion pattern analysis
282(2)
9.3.2 Interpolation in graphs
284(2)
9.3.2.1 Movie recommendation system
286(3)
9.4 Conclusions
289(4)
Bibliography
289(4)
10 Big Data Analytics for Social Networks
293(48)
Brian Baingana
Panagiotis Traganitis
Georgios Giannakis
Gonzalo Mateos
10.1 Introduction
294(2)
10.1.1 Signal processing for big data
294(1)
10.1.2 Social network analytics problems
295(1)
10.2 Visualizing and reducing dimension in social nets
296(7)
10.2.1 Kernel-based graph embedding
296(2)
10.2.2 Centrality-constraints
298(2)
10.2.3 Numerical tests
300(1)
10.2.4 Visualization of dynamic social networks
300(3)
10.3 Inference and imputation on social graphs
303(8)
10.3.1 Distributed anomaly detection for social graphs
303(1)
10.3.1.1 Anomaly detection via sparse plus low-rank decomposition
303(2)
10.3.1.2 In-network processing algorithm
305(1)
10.3.1.3 Numerical tests
306(1)
10.3.2 Prediction from partially-observed network processes
307(1)
10.3.2.1 Semi-supervised prediction of network processes
308(1)
10.3.2.2 Data-driven dictionary learning
309(1)
10.3.2.3 Numerical tests
310(1)
10.4 Unveiling communities in social networks
311(8)
10.4.1 Big data spectral clustering
312(3)
10.4.1.1 Numerical tests
315(1)
10.4.2 Robust kernel PCA
316(3)
10.4.2.1 Numerical tests
319(1)
10.5 Topology tracking from information cascades
319(11)
10.5.1 Dynamic SEMs for tracking cascades
321(1)
10.5.1.1 Model and problem statement
322(1)
10.5.1.2 Exponentially-weighted least-squares estimator
323(1)
10.5.2 Topology tracking algorithm
324(2)
10.5.2.1 Accelerated convergence
326(1)
10.5.3 Real-Time operation
327(1)
10.5.3.1 Premature termination
327(1)
10.5.3.2 Stochastic gradient descent iterations
327(1)
10.5.4 Experiments on real data
328(2)
10.6 Conclusion
330(1)
10.7 Acknowledgments
330(11)
Bibliography
330(11)
11 Semantic Model Adaptation for Evolving Big Social Data
341(50)
Nikoletta Bassiou
Constantine Kotropoulos
11.1 Introduction to Social Data Evolution
341(2)
11.2 Latent Model Adaptation
343(16)
11.2.1 Incremental Latent Semantic Analysis
343(3)
11.2.2 Incremental Probabilistic Latent Semantic Analysis
346(9)
11.2.3 Incremental Latent Dirichlet Allocation
355(4)
11.3 Incremental Spectral Clustering
359(3)
11.4 Tensor Model Adaptation
362(6)
11.4.1 Basic Tensor Concepts
362(1)
11.4.2 Incremental Tensor Analysis
363(5)
11.5 Parallel and Distributed Approaches for Big Data Analysis
368(7)
11.5.1 Parallel Probabilistic Latent Semantic Analysis
368(1)
11.5.2 Parallel Latent Dirichlet Allocation
369(2)
11.5.3 Parallel Spectral Clustering
371(2)
11.5.4 Distributed Tensor Decomposition
373(2)
11.6 Applications to Evolving Social Data Analysis
375(4)
11.6.1 Incremental Label Propagation
375(1)
11.6.2 Incremental Graph Clustering in Dynamic Social Networks
376(3)
11.7 Conclusions
379(12)
Bibliography
381(10)
12 Big Graph Storage, Processing and Visualization
391(26)
Jaroslav Pokorny
Vaclav Snasel
12.1 Introduction
391(2)
12.2 Basic Notions
393(2)
12.3 Big Graph Data Storage
395(6)
12.3.1 DBMS Architectures
395(2)
12.3.2 Graph DBMSs
397(2)
12.3.3 Storing and indexing graph structures
399(2)
12.4 Graph Data Processing
401(4)
12.4.1 Querying graphs in relational DBMS
402(1)
12.4.2 Graph querying in Datalog
403(1)
12.4.3 Query languages in graph DBMS
403(2)
12.5 Graph Data Visualization
405(4)
12.5.1 Static graph visualization
406(2)
12.5.2 Dynamic graph visualization
408(1)
12.6 Conclusions
409(8)
Bibliography
411(6)
Index 417
Prof. Ioannis Pitas (IEEE fellow, IEEE Distinguished Lecturer, EURASIP fellow) earned his PhD degree from the Department of Electrical Engineering, Aristotle University of Thessaloniki, Greece. He has been a Professor at the Department of Informatics at the same university since 1994 and has served as a visiting professor at several universities.

His current interests are in the areas of intelligent digital media, image/video processing, machine learning, and human-centered computing. He has published over 800 papers, contributed in 44 books in his areas of interest, and edited or co-authored another 10 books. He has also been a member of the program committee of many scientific conferences and workshops. In the past, he has served as an associate editor or co-editor of eight international journals and was General or Technical Chair of four international conferences. He participated in 68 R&D projects, primarily funded by the European Union and is/was principal investigator/researcher in 40 such projects. He has 20600+ citations to his work and h-index 67+ (2015).
Lisainfo e-raamatute kohta Lisainfo e-raamatute kohta
Püsilink: https://www.kriso.ee/db/97814987190562e.html
Märksõnad: