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Table of Contents
Preface; Contents; 1 Introduction; 1.1 Network Structure Analytics with Computational Intelligence; 1.1.1 Concepts of Networks; 1.1.2 Community Structure and Its Detection in Complex Networks; 1.1.3 Structure Balance and Its Transformation in Complex Networks; 1.1.4 Network Robustness and Its Optimization in Complex Networks; 1.2 Book Structure; References; 2 Network Community Discovery with Evolutionary Single-Objective Optimization; 2.1 Review of the State of the Art; 2.2 A Node Learning-Based Memetic Algorithm for Community Discovery in Small-Scale Networks
2.2.1 Memetic Algorithm with Node Learning for Community Discovery2.2.2 Problem Formation; 2.2.3 Representation and Initialization; 2.2.4 Genetic Operators; 2.2.5 The Local Search Procedure; 2.2.6 Experimental Results; 2.2.7 Conclusions; 2.3 A Multilevel Learning-Based Memetic Algorithm for Community Discovery in Large-Scale Networks; 2.3.1 Memetic Algorithm with Multi-level Learning for Community Discovery; 2.3.2 Representation and Initialization; 2.3.3 Genetic Operators; 2.3.4 Multi-level Learning Strategies; 2.3.5 Complexity Analysis of MLCD; 2.3.6 Comparisons Between MLCD and Meme-Net
2.3.7 Experimental Results2.3.8 Conclusions; 2.4 A Swarm Learning-Based Optimization Algorithm for Community Discovery in Large-Scale Networks ; 2.4.1 Greedy Particle Swarm Optimization for Network Community Discovery; 2.4.2 Particle Representation and Initialization; 2.4.3 Particle-Status-Updating Rules; 2.4.4 Particle Position Reordering; 2.4.5 Experimental Results; 2.4.6 Additional Discussion on GDPSO; 2.4.7 Conclusions; References; 3 Network Community Discovery with Evolutionary Multi-objective Optimization; 3.1 Review on the State of the Art
3.2 A Decomposition Based Multi-objective Evolutionary Algorithm for Multi-resolution Community Discovery3.2.1 Multi-objective Evolutionary Algorithm for Community Discovery; 3.2.2 Problem Formation; 3.2.3 Representation and Initialization; 3.2.4 Genetic Operators; 3.2.5 Experimental Results; 3.2.6 Conclusions; 3.3 A Multi-objective Immune Algorithm for Multi-resolution Community Discovery; 3.3.1 Multi-objective Immune Optimization for Multi-resolution Communities Identification; 3.3.2 Problem Formation; 3.3.3 Proportional Cloning; 3.3.4 Analysis of Computational Complexity
3.3.5 Experimental Results3.3.6 Conclusions; 3.4 An Efficient Multi-objective Discrete Particle Swarm Optimization for Multi-resolution Community Discovery; 3.4.1 Multi-objective Discrete Particle Swarm Optimization for Multi-resolution Community Discovery; 3.4.2 Problem Formation; 3.4.3 Definition of Discrete Position and Velocity; 3.4.4 Discrete Particle Status Updating; 3.4.5 Particle Swarm Initialization; 3.4.6 Selection of Leaders; 3.4.7 Turbulence Operator; 3.4.8 Complexity Analysis; 3.4.9 Experimental Results; 3.4.10 Experimental Results on Signed Networks; 3.4.11 Conclusions
2.2.1 Memetic Algorithm with Node Learning for Community Discovery2.2.2 Problem Formation; 2.2.3 Representation and Initialization; 2.2.4 Genetic Operators; 2.2.5 The Local Search Procedure; 2.2.6 Experimental Results; 2.2.7 Conclusions; 2.3 A Multilevel Learning-Based Memetic Algorithm for Community Discovery in Large-Scale Networks; 2.3.1 Memetic Algorithm with Multi-level Learning for Community Discovery; 2.3.2 Representation and Initialization; 2.3.3 Genetic Operators; 2.3.4 Multi-level Learning Strategies; 2.3.5 Complexity Analysis of MLCD; 2.3.6 Comparisons Between MLCD and Meme-Net
2.3.7 Experimental Results2.3.8 Conclusions; 2.4 A Swarm Learning-Based Optimization Algorithm for Community Discovery in Large-Scale Networks ; 2.4.1 Greedy Particle Swarm Optimization for Network Community Discovery; 2.4.2 Particle Representation and Initialization; 2.4.3 Particle-Status-Updating Rules; 2.4.4 Particle Position Reordering; 2.4.5 Experimental Results; 2.4.6 Additional Discussion on GDPSO; 2.4.7 Conclusions; References; 3 Network Community Discovery with Evolutionary Multi-objective Optimization; 3.1 Review on the State of the Art
3.2 A Decomposition Based Multi-objective Evolutionary Algorithm for Multi-resolution Community Discovery3.2.1 Multi-objective Evolutionary Algorithm for Community Discovery; 3.2.2 Problem Formation; 3.2.3 Representation and Initialization; 3.2.4 Genetic Operators; 3.2.5 Experimental Results; 3.2.6 Conclusions; 3.3 A Multi-objective Immune Algorithm for Multi-resolution Community Discovery; 3.3.1 Multi-objective Immune Optimization for Multi-resolution Communities Identification; 3.3.2 Problem Formation; 3.3.3 Proportional Cloning; 3.3.4 Analysis of Computational Complexity
3.3.5 Experimental Results3.3.6 Conclusions; 3.4 An Efficient Multi-objective Discrete Particle Swarm Optimization for Multi-resolution Community Discovery; 3.4.1 Multi-objective Discrete Particle Swarm Optimization for Multi-resolution Community Discovery; 3.4.2 Problem Formation; 3.4.3 Definition of Discrete Position and Velocity; 3.4.4 Discrete Particle Status Updating; 3.4.5 Particle Swarm Initialization; 3.4.6 Selection of Leaders; 3.4.7 Turbulence Operator; 3.4.8 Complexity Analysis; 3.4.9 Experimental Results; 3.4.10 Experimental Results on Signed Networks; 3.4.11 Conclusions