Computational Intelligence for Network Structure Analytics

Maoguo Gong|Qing Cai|Lijia Ma|Shanfeng Wang|Yu Lei

ISBN 10: 9811045577 / ISBN 13: 9789811045578

Verlag: Springer Singapore, 2017

Beschreibung

Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Provides a holistic view of complex network structure analytics based on computational intelligenceIncludes a rich blend of theory and practice, addressing seminal research ideas and examining the technology from a practical p. Bestandsnummer des Verkäufers 147341026

Bibliografische Details

Titel: Computational Intelligence for Network ...

Verlag: Springer Singapore

Erscheinungsdatum: 2017

Einband: Hardcover

Zustand: New

Über diesen Titel

Inhaltsangabe:

<DIV>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. &NBSP;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.</DIV><DIV><BR></DIV><DIV>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.</DIV><DIV><BR></DIV><DIV>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.</DIV>

Reseña del editor:

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.