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1 results

  • Chapter 27 - Big data and scalability

  • from Part III - Fundamentals
  • James Bagrow, University of Vermont, Yong‐Yeol Ahn, Indiana University, Bloomington
  • Book:
    Working with Network Data
    Published online:
    06 June 2024
    Print publication:
    13 June 2024, pp 447-470

  • Summary

    Networks can get big. Really big. Examples include web crawls, online social networks, and knowledge graphs. Networks from these domains can have billions of nodes and hundreds of billions of edges. Systems biology is yet another area where networks will continue to grow. As sequencing methods continue to advance, more networks and larger, denser networks will need to be analyzed. This chapter discusses some of the challenges you face and solutions you can try when scaling up to massive networks. These range from implementation details to new algorithms and strategies to reduce the burden of such big data. Various tools, such as graph databases, probabilistic data structures, and local algorithms, are at our disposal, especially if we can accept sampling effects and uncertainty.