Big Data in Complex and Social Networks

Editors: Thai, My T., Wu, Weili and Xiong,Hui
Publication Year: 2017
Publisher: CRC Press

ISBN: 978-1-49-872684-9
Category: Technology & Engineering - Technology
Image Count: 67
Book Status: Pending
Predicted Release Month: June 2017
Table of Contents

This book presents recent developments on the theoretical, algorithmic, and application aspects of Big Data in Complex and Social Networks.

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Table of Contents

  • Preface
  • Editors
  • Section I Social Networks and Complex Networks
  • Chapter 1 ■ Hyperbolic Big Data Analytics within Complex and Social Networks - ELENI STAI, VASILEIOS KARYOTIS, GEORGIOS KATSINIS, EIRINI ELENI TSIROPOULOU AND SYMEON PAPAVASSILIOU
  • 1.1 Introduction
  • 1.1.1 Scope and Objectives
  • 1.1.2 Outline
  • 1.2 Big Data and Network Science
  • 1.2.1 Complex Networks, Big Data and the Big Data Chain
  • 1.2.2 Big Data Challenges and Complex Networks
  • 1.3 Big Data Analytics based on Hyperbolic Space
  • 1.3.1 Fundamentals of Hyperbolic Geometric Space
  • 1.4 Data Correlations and Dimensionality Reduction in Hyperbolic Space
  • 1.4.1 Example
  • 1.5 Embedding of Networked Data in Hyperbolic Space and Applications
  • 1.5.1 Rigel Embedding in the Hyperboloid Model
  • 1.5.2 HyperMap Embedding
  • 1.6 Greedy Routing over Hyperbolic Coordinates and Applications within Complex and Social Networks
  • 1.7 Optimization Techniques over Hyperbolic Space for Decision-Making in Big Data
  • 1.7.1 The Case of Advertisement Allocation over Online Social Networks
  • 1.7.2 The Case of File Allocation Optimization in Wireless Cellular Networks
  • 1.8 Visualization Analytics in Hyperbolic Space
  • 1.8.1 Adaptive Focus in Hyperbolic Space
  • 1.8.2 Hierarchical (Tree) Graphs
  • 1.8.3 General Graphs
  • 1.9 Conclusions
  • Acknowledgment
  • Further Reading
  • Chapter 2 ■ Scalable Query and Analysis for Social Networks - TAK-LON (STEPHEN) WU, BINGJING ZHANG, CLAYTON DAVIS, EMILIO FERRARA, ALESSANDRO FLAMMINI, FILIPPO MENCZER AND JUDY QIU
  • 2.1 Introduction
  • 2.2 Apache High-Level Language, Syntax and its Common Features
  • 2.2.1 Pig
  • 2.2.2 Hive
  • 2.2.3 Spark SQL/Shark
  • 2.3 Pig, Hive and Spark SQL Comparison
  • 2.4 Ad-hoc Queries: Truthy and Twitter Data
  • 2.5 Iterative Scientific Applications
  • 2.5.1 K-means Clustering and PageRank
  • 2.6 Benchmarks
  • 2.6.1 Performance of Ad-hoc Queries
  • 2.6.2 Performance of Data Analysis
  • 2.7 Conclusion
  • Bibliography
  • Section II Big Data and Web Intelligence
  • Chapter 3 ■ Predicting Content Popularity in Social Networks - YAN YAN, RUIBO ZHOU, XIAOFENG GAO AND GUIHAI CHEN
  • 3.1 Introduction
  • 3.1.1 What is a Social Network?
  • 3.1.2 Levels of Social Network
  • 3.1.3 The Long Tail
  • 3.2 Classification of Social Network
  • 3.2.1 Narrow-Sensed Social Network
  • 3.2.2 News-Based Social Network
  • 3.2.3 Major-Based Social Network
  • 3.3 Prediction Model
  • 3.3.1 Feature Selection
  • 3.3.1.1 Mature Tool
  • 3.3.1.2 Correlation-based Method
  • 3.3.1.3 Unique Method
  • 3.3.2 Text Content
  • 3.3.3 Predicting Models
  • 3.3.3.1 Prediction Based on User Behaviors
  • 3.3.3.2 Prediction Based on Life Cycles
  • 3.3.3.3 Prediction Based on Network Topology
  • 3.4 Evaluation
  • 3.4.1 The Importance of Evaluation
  • 3.4.2 Evaluation Metrics
  • 3.4.2.1 Ranking Prediction
  • 3.4.2.2 Classification Prediction
  • 3.4.2.3 Numerical Prediction
  • 3.5 Look Forward
  • Bibliography
  • Chapter 4 ■ Mining User Behaviors in Large Social Networks - MENG JIANG AND PENG CUI
  • 4.1 Mining Behavioral Mechanism for Social Recommendation
  • 4.1.1 Social Contextual Factor Analysis
  • 4.1.2 Social Contextual Modeling for Recommendation
  • 4.1.3 Conclusions
  • 4.2 Mining Contextual Behavior for Prediction
  • 4.2.1 Modeling Multi-Faceted Dynamic Behaviors
  • 4.2.2 Flexible Evolutionary Multi-Faceted Analysis
  • 4.2.3 Conclusions
  • 4.3 Mining Cross-Domain Behavior for Knowledge Transfer
  • 4.3.1 Cross-Domain Behavior Modeling
  • 4.3.2 Hybrid Random Walk Algorithm
  • 4.3.3 Conclusions
  • 4.4 Summary
  • Exercises
  • Bibliography
  • Section III Security and Privacy Issues of Social Networks
  • Chapter 5 ■ Mining Misinformation in Social Media - LIANG WU, FRED MORSTATTER, XIA HU AND HUAN LIU
  • 5.1 Introduction
  • 5.2 Misinformation Modeling
  • 5.2.1 Information Diffusion in Social Networks
  • 5.2.2 Misinformation Diffusion
  • 5.3 Misinformation Identification
  • 5.3.1 Misinformation Detection
  • 5.3.2 Spreader Detection
  • 5.4 Misinformation Intervention
  • 5.4.1 Malicious Account Detection in an Early Stage
  • 5.4.2 Combating Rumors with Facts
  • 5.5 Evaluation
  • 5.5.1 Datasets
  • 5.5.2 Evaluation Metrics
  • 5.6 Conclusion and Future Work
  • Bibliography
  • Chapter 6 ■ Rumor Spreading and Detection in Online Social Networks - WEN XU AND WEILI WU
  • 6.1 Introduction
  • 6.2 Understanding Rumor Cascades
  • 6.2.1 Structure Properties of Social Networks
  • 6.2.2 Why Rumor Spreads so Fast?
  • 6.3 Rumor Source Detection: Graph Theory based Approach
  • 6.3.1 Introduction
  • 6.3.2 Detecting Multiple Rumor Sources in Networks with Partial Observations
  • 6.3.3 The Model
  • 6.3.4 The Algorithm
  • 6.3.4.1 Potential Function
  • 6.3.4.2 The Algorithm and Its Approximation Ratio
  • 6.3.5 Simulation Results
  • 6.3.6 Discussion
  • 6.4 Rumor Detection: Machine Learning based Approach
  • 6.4.1 Natural Language Processing
  • 6.4.2 Towards Information Credibility
  • 6.5 Conclusion
  • Bibliography
  • Section IV Applications
  • Chapter 7 ■ A Survey on Multilayer Networks and the Applications - HUIYUAN ZHANG, HUILING ZHANG AND MY T. THAI
  • 7.1 Introduction
  • 7.2 Network Representation
  • 7.2.1 General Representation
  • 7.2.2 Adjacency Representation
  • 7.2.3 Network Types
  • 7.2.3.1 Multiplex Network
  • 7.2.3.2 Independent Networks
  • 7.2.3.3 Interconnected Networks
  • 7.2.3.4 Multidimensional Networks
  • 7.2.3.5 Multilevel Networks
  • 7.2.3.6 Temporal Networks
  • 7.2.3.7 Hypernetworks
  • 7.3 Dynamics in Multilayer Networks
  • 7.3.1 Diffusion Spreading in Multilayer Networks
  • 7.3.2 Diffusion Models in Multilayer Networks
  • 7.3.2.1 Threshold Model
  • 7.3.2.2 Cascading Model
  • 7.3.2.3 SIR Model
  • 7.3.3 Network Aggregation and Synchronization
  • 7.4 Network Structure and Measurements
  • 7.4.1 Node Degree
  • 7.4.2 Betweenness
  • 7.4.3 Clustering and Transitivity
  • 7.4.4 Walks and Paths
  • 7.4.5 Matrices and Spectral Properties
  • 7.5 Applications
  • 7.5.1 Social Networks
  • 7.5.2 Computer Science Networks
  • 7.5.3 Transportation Networks
  • 7.5.4 Power Grids
  • 7.5.5 Economical Networks
  • 7.6 Conclusions
  • Bibliography
  • Chapter 8 ■ Exploring Legislative Networks in a Multiparty System - JOSE MANUEL MAGALLANES
  • 8.1 Introduction
  • 8.2 Background
  • 8.2.1 Political Scenario
  • 8.2.2 Institutional Conditions
  • 8.2.3 The 2006-2011 Congress
  • 8.3 Co-Sponsorship as a Network
  • 8.4 Bill Co-Sponsorship and Social Network Analysis
  • 8.4.1 Organizing the Data
  • 8.4.2 Re-Election Strategies
  • 8.4.3 Party Splitting and Switching
  • 8.5 Discussion of Results
  • 8.6 Further Research
  • Acknowledgment
  • Bibliography