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Computational Intelligence for Network Structure Analytics [electronic resource] / by Maoguo Gong, Qing Cai, Lijia Ma, Shanfeng Wang, Yu Lei.
Computational Intelligence for Network Structure Analytics [electronic resource] / by Maoguo Gong, Qing Cai, Lijia Ma, Shanfeng Wang, Yu Lei.
Creatore [Gong, Maoguo]
Estensione 1 online resource (XI, 283 p. 159 illus., 140 illus. in color.) online resource.
Disciplina 006.3
Accesso persona Cai, Qing
Ma, Lijia
Wang, Shanfeng
Lei, Yu.
Accesso ente SpringerLink (Online service)
Genere/Forma Electronic books
ISBN 9789811045585
9789811045578
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Introduction -- Network Community Discovery with Evolutionary Single-objective Optimization -- Network Community Discovery with Evolutionary Multi-objective Optimization -- Network Structure Balance Analytics with Evolutionary Optimization -- Network Robustness Analytics with Optimization -- Real-world Cases of Network Structure Analytics -- Concluding Remarks.
Record Nr. DUKE-008106298
[Gong, Maoguo]  
Materiale a stampa
Lo trovi qui: Duke University
Computational intelligence for network structure analytics [electronic resource] / Maoguo Gong, Qing Cai, Lijia Ma, Shanfeng Wang, Yu Lei.
Computational intelligence for network structure analytics [electronic resource] / Maoguo Gong, Qing Cai, Lijia Ma, Shanfeng Wang, Yu Lei.
Estensione 1 online resource.
Disciplina 006.3
004
Accesso persona Gong, Maoguo
Cai, Qing
Ma, Lijia
Wang, Shanfeng
Lei, Yu.
ISBN 9789811045585
9811045585
9789811045578
9811045577
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UALBERTA-8063470
Materiale a stampa
Lo trovi qui: University of Alberta / NEOS Library Consortium
Computational intelligence for network structure analytics [electronic resource] / Maoguo Gong, Qing Cai, Lijia Ma, Shanfeng Wang, Yu Lei.
Computational intelligence for network structure analytics [electronic resource] / Maoguo Gong, Qing Cai, Lijia Ma, Shanfeng Wang, Yu Lei.
Estensione 1 online resource.
Accesso persona Gong, Maoguo
Cai, Qing
Ma, Lijia
Wang, Shanfeng
Lei, Yu.
Genere/Forma Electronic books
ISBN 9789811045585
9811045585
9789811045578
9811045577
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 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
Record Nr. UCHICAGO-11361583
Materiale a stampa
Lo trovi qui: University of Chicago
Computational Intelligence for Network Structure Analytics [electronic resource] / by Maoguo Gong, Qing Cai, Lijia Ma, Shanfeng Wang, Yu Lei.
Computational Intelligence for Network Structure Analytics [electronic resource] / by Maoguo Gong, Qing Cai, Lijia Ma, Shanfeng Wang, Yu Lei.
Creatore [Gong, Maoguo]
Estensione XI, 283 p. 159 illus., 140 illus. in color : online resource.
Disciplina 006.3
Accesso persona Cai, Qing
Ma, Lijia
Wang, Shanfeng
Lei, Yu.
Accesso ente SpringerLink (Online service)
ISBN 9789811045585
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Introduction -- Network Community Discovery with Evolutionary Single-objective Optimization -- Network Community Discovery with Evolutionary Multi-objective Optimization -- Network Structure Balance Analytics with Evolutionary Optimization -- Network Robustness Analytics with Optimization -- Real-world Cases of Network Structure Analytics -- Concluding Remarks.
Record Nr. YALE-13305885
[Gong, Maoguo]  
Materiale a stampa
Lo trovi qui: Yale University
Computational intelligence for network structure analytics [electronic resource] / Maoguo Gong, Qing Cai, Lijia Ma, Shanfeng Wang, Yu Lei.
Computational intelligence for network structure analytics [electronic resource] / Maoguo Gong, Qing Cai, Lijia Ma, Shanfeng Wang, Yu Lei.
Estensione 1 online resource.
Disciplina 006.3
004
Accesso persona Gong, Maoguo
Cai, Qing
Ma, Lijia
Wang, Shanfeng
Lei, Yu.
Genere/Forma Electronic books
ISBN 9789811045585
9811045585
9789811045578
9811045577
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 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
Record Nr. NYU-006091338
Materiale a stampa
Lo trovi qui: New York University