From Security to Community Detection in Social Networking Platforms

In: Lecture Notes in Social Networks Ser.

Abstract

Intro -- Preface -- Introduction -- From Security to Community Detection in Social Networking Platforms -- Contents -- Real-World Application of Ego-Network Analysis to Evaluate Environmental Management Structures -- 1 Introduction -- 2 Methods -- 2.1 Study Areas -- 2.2 Research Design and Data Collection -- 3 Concepts and Methodology -- 4 Research Findings and Discussion -- 4.1 Structure of the Ego-Networks -- 4.2 Correlation Between the Indegree and Betweenness Values of the Networks -- 4.3 Consequences of Removing the Ego: Role of Simmelian Ties and Girvan-Newman Clustering -- 5 Conclusions and Future Work -- References -- An Evolutionary Approach for Detecting Communities in Social Networks -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 The Algorithm -- 3.2 Encoding and Initialization -- 3.3 Fitness Function -- 3.4 Crossover -- 3.5 Mutation -- 3.6 Selection -- 3.7 Preprocess -- 4 Experimental Results and Discussion -- 4.1 Datasets -- 4.2 Comparison Between Genetic Algorithms -- 4.3 Comparison with Different Community Detection Algorithms for Social Networks -- 4.4 Experiments with Using Preprocess Step -- 5 Conclusion -- References -- On Detecting Multidimensional Communities -- 1 Introduction -- 1.1 Related Approaches -- 1.2 Motivations and Contributions -- 2 The MCDA Approach -- 2.1 Problem Statement -- 2.2 Outlier Handling -- 2.2.1 Step 1: Estimating Outlier Scores -- 2.2.2 Step 2: Automatic Identification of Outliers -- 2.3 Summary of Outlier Handling Procedure -- 2.4 Mining Community Structures -- 2.4.1 Community Detection Approach -- 2.4.2 Selection of Relevant Dimensions of Communities -- 2.5 Summary of the MCDA Approach -- 3 Experimental Results -- 3.1 Experiments on Synthetic Networks -- 3.1.1 Synthetic Network Generation -- 3.1.2 Community Detection Accuracy -- 3.1.3 Outlier Immunity -- 3.1.4 Scalability Experiments.