國立高雄大學圖資館 |

Language: English

Back

Computational intelligence for netwo...

Gong, Maoguo.

Computational intelligence for network structure analytics

Record Type:	Electronic resources : Monograph/item
Title/Author:	Computational intelligence for network structure analyticsby Maoguo Gong ... [et al.].
other author:	Gong, Maoguo.
Published:	Singapore :Springer Singapore :2017.
Description:	xi, 283 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Computational intelligence.
Online resource:	http://dx.doi.org/10.1007/978-981-10-4558-5
ISBN:	9789811045585$q(electronic bk.)

Computational intelligence for network structure analytics
Computational intelligence for network structure analytics[electronic resource] /by Maoguo Gong ... [et al.]. - Singapore :Springer Singapore :2017. - xi, 283 p. :ill., digital ;24 cm.

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.

This 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.

ISBN: 9789811045585$q(electronic bk.)

Standard No.: 10.1007/978-981-10-4558-5doiSubjects--Topical Terms:

210824
Computational intelligence.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Computational intelligence for network structure analytics
LDR:02857nmm a2200325 a 4500 001 522125
003 DE-He213
005 20170919184635.0
006 m d
007 cr nn 008maaau
008 180521s2017 si s 0 eng d
020 $a 9789811045585$q(electronic bk.)
020 $a 9789811045578$q(paper)
024 7 $a 10.1007/978-981-10-4558-5 $2 doi
035 $a 978-981-10-4558-5
040 $a GP $c GP
041 0 $a eng
050 4 $a Q342
072 7 $a UYQ $2 bicssc
072 7 $a TJFM1 $2 bicssc
072 7 $a COM004000 $2 bisacsh
082 0 4 $a 006.3 $2 23
090 $a Q342 $b .C738 2017
245 0 0 $a Computational intelligence for network structure analytics $h [electronic resource] / $c by Maoguo Gong ... [et al.].
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2017.
300 $a xi, 283 p. : $b ill., digital ; $c 24 cm.
505 0 $a 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.
520 $a This 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 $a Computational intelligence. $3 210824
650 1 4 $a Computer Science. $3 212513
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 252959
650 2 4 $a Optimization. $3 274084
650 2 4 $a Complex Systems. $3 558544
650 2 4 $a Theory of Computation. $3 274475
650 2 4 $a Data Mining and Knowledge Discovery. $3 275288
700 1 $a Gong, Maoguo. $3 732609
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-981-10-4558-5
950 $a Computer Science (Springer-11645)