Trends in Social Network Analysis : Information Propagation User Behavior Modeling Forecasting and Vulnerability Assessment 2.4 Pragmatic Feature 2.5 Hyperbolic Feature 3 Preliminaries 3.1 System Model 3.2 Part of Speech (POS) Tagging 3.3 Parse Tree Generation 4 Data Collection and Preprocessing 4.1 Data Collection 4.2 Preprocessing 5 Proposed Scheme 5.1 PBLGA 5.2 LDC 5.3 TCUF 5.4 TCTDP 6 Classifiers 7 Results and Discussion 7.1 Experimental Results 8 Conclusion References The DEvOTION Algorithm for Delurking in Social Networks 1 Introduction 2 Targeted Influence Maximization 3 Delurking Oriented Targeted Influence Maximization 3.1 Problem Statement 3.2 Identifying and Characterizing Lurkers 3.3 Choosing the Information Diffusion Model 3.4 Properties of the Proposed Objective Function 3.5 Modeling the Information Diffusion Graph 3.6 The DEvOTION Algorithm 4 Experimental Evaluation 4.1 Evaluation Methodology 4.2 Experimental Setting 4.3 Data 5 Results 5.1 Impact of Parameters in DEvOTION 5.2 Comparison with Baselines 5.3 Comparison with Influence Maximization Algorithms 5.4 Comparison with KB TIM 5.5 Seed Characteristics 5.6 Discussion 6 Conclusions and Future Work References Social Engineering Threat Assessment Using a Multi Layered Graph Based Model 1 Introduction 2 Related Work 2.1 Vulnerability Assessment 2.2 Multilayer Graphs for Social Networks 3 A Multi Layered Model for Social Engineering Vulnerability Assessment 4 Case Studies 4.1 Data Collection 4.2 Detection of Stolen Credit Card Resellers 4.3 Threat Assessment from a Social Engineering Attack and a Reverse Social Engineering Attack 4.3.1 Cross Site Profile Cloning 4.3.2 Recommendation System Based Attack 4.3.3 Evaluation of These Attacks 4.4 Detection of Vulnerable Users to a Social Engineering Attack 5 Conclusion References Preface Contents Contributors The Perceived Assortativity of Social Networks: Methodological Problems and Solutions 1 Introduction 2 Assortativity in Social and Other Networks 2.1 Literature Search: Method 2.2 Literature Search: Results 2.3 Literature Search: Conclusions 3 Methodological Pitfalls and False Assortativity 3.1 Group Based Networks and Assortativity 3.2 Modeling Group Based Sampling 3.3 Filtering Networks 4 Solutions 4.1 Increased Sampling 4.2 Use of Null Models 4.3 Analysing Weighted Networks 4.4 Using Diadic Over Group Based Approaches 4.5 Modern Technology 4.6 Alternatives to the Newman Degree Correlation Measure 5 Conclusions References A Parametric Study to Construct Time Aware Social Profiles 1 Introduction 2 Related Works 2.1 User Profile Building Process 2.2 Incorporating Dynamic Interests in the Profile 2.3 Social Network Evolution 3 Proposition: Temporal Scores to Construct Social Profiles 3.1 Notations 3.2 * 0.9pc 3.3 Community Based Social Profile Construction Process with Temporal Score 3.3.1 Temporal Score Calculation 3.3.2 Temporal Score Integration 4 Experiments 4.1 Dataset Description 4.2 Analysis of Common Keywords Between DBLP and Mendeley 4.3 Case Study 4.3.1 Ground Truth: Extraction of the Real User Profile from Mendeley 4.3.2 Social Profiles Construction and Parametric Study 4.4 Results 4.4.1 All Users Results 4.4.2 Results for Selected Users 4.4.3 Different Time Decay Rate for the Relationships and the Information 4.4.4 Discussion 5 Conclusion and Future Works Appendix References Sarcasm Analysis on Twitter Data Using Machine LearningApproaches 1 Introduction 2 Related Work 2.1 Machine Learning Based Approach 2.2 Corpus Based 2.3 Lexical Features Through the Grapevine: A Comparison of News in Microblogs and Traditional Media 1 Introduction 2 Related Work 2.1 Microblogs and Traditional Media 2.2 Newsworthiness 2.3 Content Similarity 2.4 Network Measures and Metrics 2.5 Topical Similarity and Object Association 3 Content Similarity Based Approach 3.1 Data Collection 3.2 Similarity Computation 3.3 Strategy Selection 3.4 Experimental Setup 3.4.1 Random Sampling 3.4.2 Niche Content Extraction 3.4.3 User Study 3.5 Evaluation 3.5.1 Frequency Analysis 3.5.2 Study Participants and Procedure 3.5.3 Results 4 Network Based Approach 4.1 Hypotheses 4.2 Data Collection and Preprocessing 4.3 Labeling Tweets 4.4 Network Analysis (Exp 1) 4.5 Topic Association (Exp 2) 4.6 Results and Discussions 4.6.1 Network Analysis (Exp 1) 4.6.2 Topic Association (Exp 2) 5 Discussion and Future Work 5.1 Scalability and Real Time News Gathering 6 Conclusion References Prediction of Elevated Activity in Online Social Media Using Aggregated and Individualized Models 1 Introduction 2 Related Work 3 Methodology 3.1 Seasonality 3.2 Aggregate Autoregressive Model 3.3 Aggregation of Causal State Models 3.4 Time Complexity of Each Model 4 Data Collection and Selection of U 5 Results 5.1 Adjustment to the Aggregation of Individuals Model 5.2 Predicting Activation Level at Varying Thresholds 5.3 Utility of Individual Level Models Beyond Aggregate Prediction 6 Conclusion References Unsupervised Link Prediction Based on Time Frames in Weighted Directed Citation Networks 1 Introduction 2 Related Works 3 Link Prediction 4 Traditional Proximity Metrics 4.1 Number of Common Neighbors (CN) 4.2 Jacquards Coefficient 4.3 Preferential Attachment (PA) 4.4 Adamic Adar Coefficient (AA) 5 Citation Network and Triad Patterns 6 The Proposed Time Frame Based Link Prediction in Directed Networks 6.1 Temporal Structure 6.2 Unweighted and Weighted Temporal Events 6.2.1 Protective 6.2.2 W Protective 6.2.3 Inventive 6.2.4 W Inventive 6.2.5 Regressive 6.2.6 W Regressive 6.3 Time Frame Based Score 7 Experimental Results 8 Conclusions References An Approach to Maximize the Influence Spread in the Social Networks 1 Introduction 2 Related Works 3 Our Contribution 4 Spanning Graph 4.1 Centrality Measure 4.2 SCG Algorithm 4.2.1 SCGv1 Algorithm 4.2.2 SCGv2 Algorithm 4.3 SG Algorithm 5 Propagation Model 5.1 Linear Threshold Model 5.2 Independent Cascade Model 6 Experiment and Results 6.1 Data 6.2 Benchmark Heuristics and Parameters 6.3 Results 7 Conclusion References Energy Efficiency Analysis of the Very Fast Decision Tree Algorithm 1 Introduction 2 Background 2.1 Energy Awareness 2.2 Data Stream Mining 2.3 Terminology 3 Related Work 4 Theoretical Analysis 5 Experimental Design 5.1 Problem Definition 5.2 Data Gathering 5.3 Methodology 5.3.1 Parameter Choice 5.3.2 Procedure 5.3.3 Tools 5.4 Evaluation 6 Results and Analysis 6.1 General Analyses 6.2 Parameter Analysis 6.2.1 Parameter nmin 6.2.2 Parameter τ 6.2.3 Parameter δ 6.2.4 Parameter MEM1 6.2.5 Parameter MEM2 6.2.6 Parameter S2 6.2.7 Parameter RPA 6.2.8 Summary of the Parameter Analysis 7 Conclusions and Future Work References Glossary