A new method provides better insights into real-world network evolution

phys.org | 9/13/2017 | Staff
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Nature and society are full of so-called real-world complex systems, such as protein interactions. Theoretical models, called complex networks, describe them and consist of nodes representing any basic element of that network, and links describing interactions or reactions between two nodes.

In the case of protein-interaction studies, reconstruction of complex networks is key as the data available is often inaccurate and our knowledge of the exact nature of these interactions is limited. For reconstruction of networks, link predict—the likelihood of the existence of a link between two nodes—matters. Now, Chinese scientists have looked at the influence of the network structure to shed some light on the robustness of the latest methods used to predict the behaviour of such complex networks.

Yang - Xiao-Dong - Zhang - Shanghai - Jiao

Jin-Xuan Yang and Xiao-Dong Zhang from Shanghai Jiao Tong University in China have just published their work in EPJ B, providing a good reference for the choice of a suitable algorithm for link prediction depending...
(Excerpt) Read more at: phys.org
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