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GRAPH-BASED SOCIAL MEDIA ANALYSIS
Título:
GRAPH-BASED SOCIAL MEDIA ANALYSIS
Subtítulo:
Autor:
PITAS, I
Editorial:
CRC PRESS
Año de edición:
2016
Materia
REDES SOCIALES
ISBN:
978-1-4987-1904-9
Páginas:
424
99,50 €

 

Sinopsis

Features

Focuses on the mathematical foundations of social media analysis
Covers significant new trends, such as big data analysis and social media evolution
Provides an overview of social media structure
Explains complex social media phenomena, such as information diffusion and marketing and recommendation systems
Presents various approaches to storing vast amounts of data online and retrieving them in real-time
Summary

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.



Table of Contents

Graphs in Social and Digital Media
Alexandros Iosifidis, Nikolaos Tsapanos, and Ioannis Pitas
Introduction
Dominant Social Networking/Media Platforms
Collecting Data from Social Media Sites
Social Media Graphs
Graph Storage Formats and Visualization
Big Data Issues in Social and Digital Media
Distributed Computing Platforms
Conclusions
Bibliography

Mathematical Preliminaries: Graphs and Matrices
Nikolaos Tsapanos, Alexandros Iosifidis, and Ioannis Pitas
Graph Basics
Linear Algebra Tools
Matrix Decompositions
Vector and Matrix Derivatives
Bibliography

Algebraic Graph Analysis
Nikolaos Tsapanos, Anastasios Tefas, and Ioannis Pitas
Introduction
Spectral Graph Theory
Applications of Graph Analysis
Random Graph Generation
Graph Clustering
Graph Matching
Random Walks
Graph Anomaly Detection
Conclusions
Bibliography

Web Search Based on Ranking
Andrea Tagarelli and Santosh Kabbur, and George Karypis
Introduction
Information Retrieval Background
Relevance Beyond the Web Page Text
Centrality and Prestige
Topic-Sensitive Ranking
Ranking in Heterogeneous Networks
Organizing Search Results
Conclusion
Bibliography

Label Propagation and Information Diffusion in Graphs
Eftychia Fotiadou, Olga Zoidi, and Ioannis Pitas
Introduction
Graph Construction Approaches
Label Inference Methods
Diffusion Processes
Social Network Diffusion Models
Conclusions
Bibliography

Graph-Based Pattern Classification and Dimensionality Reduction
Alexandros Iosifidis and Ioannis Pitas
Introduction
Notations
Unsupervised Methods
Supervised Methods
Semi-supervised Methods
Applications
Conclusions
Bibliography

Matrix and Tensor Factorization with Recommender System Applications
Panagiotis Symeonidis
Introduction
Singular Value Decomposition on Matrices for Recommender Systems
Higher Order Singular Value Decomposition (HOSVD) on Tensors
A Real Geo-Social System Based on HOSVD
Conclusion
Bibliography

Multimedia Social Search Based on Hypergraph Learning
Constantine Kotropoulos
Introduction
Hypergraphs
Game-Theoretic Approaches to Uniform Hypergraph Clustering
Spectral Clustering for Arbitrary Hypergraphs
Ranking on Hypergraphs
Applications
Big Data: Randomized Methods for Matrix/Hypermatrix Decompositions
Conclusions
Acknowledgments
Bibliography

Graph Signal Processing in Social Media
Sunil Narang
Motivation
Graph Signal Processing (GSP)
Applications
Conclusions
Bibliography

Big Data Analytics for Social Networks
Brian Baingana, Panagiotis Traganitis, Georgios Giannakis, and Gonzalo Mateos
Introduction
Visualizing and Reducing Dimension in Social Nets
Inference and Imputation on Social Graphs
Unveiling Communities in Social Networks
Topology Tracking from Information Cascades
Conclusion
Acknowledgments
Bibliography

Semantic Model Adaptation for Evolving Big Social Data
Nikoletta Bassiou and Constantine Kotropoulos
Introduction to Social Data Evolution
Latent Model Adaptation
Incremental Spectral Clustering
Tensor Model Adaptation
Parallel and Distributed Approaches for Big Data Analysis
Applications to Evolving Social Data Analysis
Conclusions
Bibliography

Big Graph Storage, Processing and Visualization
Jaroslav Pokorny and Vaclav Snasel
Introduction
Basic Notions
Big Graph Data Storage
Graph Data Processing
Graph Data Visualization
Conclusions
Bibliography

Index