3rd International Winter School and Conference on Network Science: NetSci-X 2017

Baruch Barzel (Editor), Rami Puzis (Editor), Erez Shmueli (Editor)

Research output: Book/ReportBookpeer-review


This book contains original research chapters related to the interdisciplinary field of complex networks spanning biological and environmental networks, social, technological, and economic networks. Many natural phenomena can be modeled as networks where nodes are the primitive compounds and links represent their interactions, similarities, or distances of sorts. Complex networks have an enormous impact on research in various fields like biology, social sciences, engineering, and cyber-security to name a few. The topology of a network often encompasses important information on the functionality and dynamics of the system or the phenomenon it represents. Network science is an emerging interdisciplinary discipline that provides tools and insights to researchers in a variety of domains. NetSci-X is the central winter conference within the field and brings together leading researchers and innovators to connect, meet, and establish interdisciplinary channels for collaboration. It is the largest and best known event in the area of network science. This text demonstrates how ideas formulated by authors with different backgrounds are transformed into models, methods, and algorithms that are used to study complex systems across different domains and will appeal to researchers and students within in the field. .
Original languageEnglish
Place of PublicationCham
PublisherSpringer International Publishing; Imprint: Springer
Number of pages130
Edition1st ed. 2017
ISBN (Electronic)3319554719
StatePublished - 2017

Publication series

NameSpringer Proceedings in Complexity
PublisherSpringer International Publishing; Imprint: Springer
ISSN (Electronic)2213-8684


  • protein interactions
  • transportation networks
  • social networks
  • communication networks
  • network science proceedings
  • NetSciX
  • computational social sciences
  • data-driven science, modeling and theory building


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