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.
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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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Škrlj, B., Kralj, J., Lavrač, N. (2019). Py3plex: A Library for Scalable Multilayer Network Analysis and Visualization. In: Aiello, L., Cherifi, C., Cherifi, H., Lambiotte, R., Lió, P., Rocha, L. (eds) Complex Networks and Their Applications VII. COMPLEX NETWORKS 2018. Studies in Computational Intelligence, vol 812. Springer, Cham. https://doi.org/10.1007/978-3-030-05411-3_60
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