Revealing the Hidden Language of Complex Networks

By Ömer Nebil Yaveroğlu, Noël Malod-Dognin, Darren R. Davis, Zoran Levnajic, Vuk Janjic, R. Karapandža, Aleksandar Stojmirovic, Natasa Przulj. 2014

234 citations recorded by the source corpus.

Sophisticated methods for analysing complex networks promise to be of great benefit to almost all scientific disciplines, yet they elude us. In this work, we make fundamental methodological advances to rectify this. We discover that the interaction between a small number of roles, played by nodes in a network, can characterize a network's structure and also provide a clear real-world interpretation. Given this insight, we develop a framework for analysing and comparing networks, which outperforms all existing ones. We demonstrate its strength by uncovering novel relationships between seemingly unrelated networks, such as Facebook, metabolic and protein structure networks. We also use it to track the dynamics of the world trade network, showing that a country's role of a broker between non-trading countries indicates economic prosperity, whereas peripheral roles are associated with poverty. This result, though intuitive, has escaped all existing frameworks. Finally, our approach translates network topology into everyday language, bringing network analysis closer to domain scientists.

Subfields: Molecular Biology, Statistical and Nonlinear Physics

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Citations in the corpus

Motifs in Temporal Networks (2016, background): “Our work builds upon the rich literature on network motifs in static graphs, where these models have proved crucial to understanding the mechanisms driving complex systems [19] and to characterizing classes of static networks [25, 29].”
Local Higher-Order Graph Clustering (2017, background): “Such higher-order connectivity structures are crucial to the organization of complex networks [5, 35, 48], and it remains an open question how network motifs can be incorporated into local clustering frameworks.”