  Systematic relations between multiple objects that occur in various fields
can be represented as networks. Real-world networks typically exhibit complex
topologies whose structural properties are key factors in characterizing and
further exploring the networks themselves. Uncertainty, modelling procedures
and measurement difficulties raise often insurmountable challenges in fully
characterizing most of the known real-world networks; hence, the necessity to
predict their unknown elements from the limited data currently available in
order to estimate possible future relations and/or to unveil unmeasurable
relations. In this work, we propose a deep learning approach to this problem
based on Graph Convolutional Networks for predicting networks while preserving
their original structural properties. The study reveals that this method can
preserve scale-free and small-world properties of complex networks when
predicting their unknown parts, a feature lacked by the up-to-date conventional
methods. An external validation realized by testing the approach on biological
networks confirms the results, initially obtained on artificial data. Moreover,
this process provides new insights into the retainability of network structure
properties in network prediction. We anticipate that our work could inspire
similar approaches in other research fields as well, where unknown mechanisms
behind complex systems need to be revealed by combining machine-based and
experiment-based methods.
