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Py3plex: A Library for Scalable Multilayer Network Analysis and Visualization

  • Blaž Škrlj
  • Jan Kralj
  • Nada Lavrač
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 812)

Abstract

Real-life systems are commonly represented as networks of interacting entities. While homogeneous networks consist of nodes of a single node type, multilayer networks are characterized by multiple types of nodes or edges, all present in the same system. Analysis and visualization of such networks represent a challenge for real-life complex network applications. The presented Py3plex Python-based library facilitates the exploration and visualization of multilayer networks. The library includes a diagonal projection-based network visualization, developed specifically for large networks with multiple node (and edge) types. The library also includes state-of-the-art methods for network decomposition and statistical analysis. The Py3plex functionality is showcased on real-world multilayer networks from the domains of biology and on synthetic networks.

Notes

Acknowledgements

This work was financially supported by the Slovenian Research Agency (ARRS) grants HinLife: Analysis of Heterogeneous Information Networks for Knowledge Discovery in Life Sciences (J7-7303) and Semantic Data Mining for Linked Open Data (financed under the ERC Complementary Scheme, N2-0078).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Jožef Stefan International Postgratuate SchoolLjubljanaSlovenia
  2. 2.Jožef Stefan InstituteLjubljanaSlovenia
  3. 3.University of Nova GoricaVipavaSlovenia

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