國立虎尾科技大學 |

Computational intelligence for network structure analytics

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Computational intelligence for network structure analytics/ by Maoguo Gong ... [et al.].
其他作者:	Gong, Maoguo.
出版者:	Singapore :Springer Singapore : : 2017.,
面頁冊數:	xi, 283 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
標題:	Computational intelligence. -
電子資源:	http://dx.doi.org/10.1007/978-981-10-4558-5
ISBN:	9789811045585

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

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

568984
Computational intelligence.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Computational intelligence for network structure analytics
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245 0 0 $a Computational intelligence for network structure analytics $h [electronic resource] / $c by Maoguo Gong ... [et al.].
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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.
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