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2079 results in Statistical Physics

Page 7 of 104

Data Science for Complex Systems
  • Anindya S. Chakrabarti, K. Shuvo Bakar, Anirban Chakraborti
  • Published online:
    11 May 2023
    Print publication:
    25 May 2023
  • Many real-life systems are dynamic, evolving, and intertwined. Examples of such systems displaying 'complexity', can be found in a wide variety of contexts ranging from economics to biology, to the environmental and physical sciences. The study of complex systems involves analysis and interpretation of vast quantities of data, which necessitates the application of many classical and modern tools and techniques from statistics, network science, machine learning, and agent-based modelling. Drawing from the latest research, this self-contained and pedagogical text describes some of the most important and widely used methods, emphasising both empirical and theoretical approaches. More broadly, this book provides an accessible guide to a data-driven toolkit for scientists, engineers, and social scientists who require effective analysis of large quantities of data, whether that be related to social networks, financial markets, economies or other types of complex systems.

  • 6 - Network Visualization

  • Tom Brughmans, Aarhus Universitet, Denmark, Matthew A. Peeples, Arizona State University
  • Book:
    Network Science in Archaeology
    Published online:
    06 April 2023
    Print publication:
    13 April 2023, pp 193-236

  • Summary

    When many people (network researchers included) think about networks, the first thing that pops into their head is the classic network node-link diagram. In its simplest form a network graph is just a collection of points on a page representing entities of some sort with lines drawn to indicate the connections among those entities. Network visuals can be small and include only a few actors and relations where structural patterns and positions can be clearly observed. They can also be dizzyingly complex bundles of thousands, tens of thousands, or more entities and connections where general textures of relations and topological features might be visible but the positions of most nodes and edges are obscured by complexity. In either case, such visuals can paint a fascinating picture of a dataset and help a researcher recognize, interpret, and explain patterns in all manner of relational data that would otherwise be difficult to identify or communicate even with the myriad of network metrics available.

Page 7 of 104