Complex network prediction using deep learning

TL;DR

This paper introduces a deep learning method using Graph Convolutional Networks to predict missing parts of complex networks while maintaining their key structural properties, validated on biological data.

Contribution

The study presents a novel application of Graph Convolutional Networks for network prediction that preserves structural properties like scale-free and small-world features.

Findings

Preserves scale-free properties in network prediction

Maintains small-world characteristics during prediction

Validated on biological networks confirming artificial data results

Abstract

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.