Computational Intelligence for Network Structure Analytics / by Maoguo Gong, Qing Cai, Lijia Ma, Shanfeng Wang, Yu Lei.

Format:

Book

Author/Creator:

Gong, Maoguo, author.

Cai, Qing, author.

Ma, Lijia, author.

Wang, Shanfeng, author.

Lei, Yu, author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Language:

English

Subjects (All):

Artificial intelligence.

Mathematical optimization.

System theory.

Computers.

Data mining.

Artificial Intelligence.

Optimization.

Complex Systems.

Theory of Computation.

Data Mining and Knowledge Discovery.

Local Subjects:

Artificial Intelligence.

Optimization.

Complex Systems.

Theory of Computation.

Data Mining and Knowledge Discovery.

Physical Description:

1 online resource (XI, 283 pages) : 159 illustrations, 140 illustrations in color

Edition:

First edition 2017.

Contained In:

Springer eBooks

Place of Publication:

Singapore : Springer Singapore : Imprint: Springer, 2017.

System Details:

text file PDF

Summary:

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, et cetera, 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.

Contents:

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.

Other Format:

Printed edition:

ISBN:

978-981-10-4558-5

9789811045585

Access Restriction:

Restricted for use by site license.