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

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