MARC record from Internet Archive

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024 7 $a10.1007/978-981-10-4558-5$2doi
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245 00 $aComputational intelligence for network structure analytics /$cMaoguo Gong, Qing Cai, Lijia Ma, Shanfeng Wang, Yu Lei.
260 $aSingapore :$bSpringer,$c2017.
300 $a1 online resource
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
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505 0 $aPreface; 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.
505 8 $a2.2.1 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.
505 8 $a2.3.7 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.
505 8 $a3.2 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.
505 8 $a3.3.5 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.
504 $aIncludes bibliographical references.
588 0 $aPrint version record.
520 $aThis book presents the latest research advances in complex network structure analytics based on computational intelligence (CI) approaches, particularly evolutionary optimization. Most if not all network issues are actually optimization problems, which are mostly NP-hard and challenge conventional optimization techniques. To effectively and efficiently solve these hard optimization problems, CI based network structure analytics offer significant advantages over conventional network analytics techniques. Meanwhile, using CI techniques may facilitate smart decision making by providing multiple options to choose from, while conventional methods can only offer a decision maker a single suggestion. In addition, CI based network structure analytics can greatly facilitate network modeling and analysis. And employing CI techniques to resolve network issues is likely to inspire other fields of study such as recommender systems, system biology, etc., which will in turn expand CI's scope and applications. As a comprehensive text, the book covers a range of key topics, including network community discovery, evolutionary optimization, network structure balance analytics, network robustness analytics, community-based personalized recommendation, influence maximization, and biological network alignment. Offering a rich blend of theory and practice, the book is suitable for students, researchers and practitioners interested in network analytics and computational intelligence, both as a textbook and as a reference work.
650 0 $aComputational intelligence.
650 7 $aCOMPUTERS$xGeneral.$2bisacsh
650 7 $aComputational intelligence.$2fast$0(OCoLC)fst00871995
655 4 $aElectronic books.
700 1 $aGong, Maoguo.
700 1 $aCai, Qing.
700 1 $aMa, Lijia.
700 1 $aWang, Shanfeng.
700 1 $aLei, Yu.
776 08 $iPrint version:$tComputational intelligence for network structure analytics.$dSingapore : Springer, 2017$z9789811045578$z9811045577$w(OCoLC)978290376
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856 40 $3SpringerLink$uhttps://link.springer.com/book/10.1007/978-981-10-4558-5
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