Reconstructing the evolution history of networked complex systems

Junya Wang, Yi-Jiao Zhang, Cong Xu, Jiaze Li, Jiachen Sun, Jiarong Xie, Ling Feng, Tianshou Zhou and Yanqing Hu ()
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Junya Wang: Sun Yat-sen University
Yi-Jiao Zhang: Southern University of Science and Technology
Cong Xu: Southern University of Science and Technology
Jiaze Li: Maastricht University
Jiachen Sun: Tencent Inc.
Jiarong Xie: Beijing Normal University
Ling Feng: Technology and Research (A*STAR)
Tianshou Zhou: Sun Yat-sen University
Yanqing Hu: Southern University of Science and Technology

Nature Communications, 2024, vol. 15, issue 1, 1-11

Abstract: Abstract The evolution processes of complex systems carry key information in the systems’ functional properties. Applying machine learning algorithms, we demonstrate that the historical formation process of various networked complex systems can be extracted, including protein-protein interaction, ecology, and social network systems. The recovered evolution process has demonstrations of immense scientific values, such as interpreting the evolution of protein-protein interaction network, facilitating structure prediction, and particularly revealing the key co-evolution features of network structures such as preferential attachment, community structure, local clustering, degree-degree correlation that could not be explained collectively by previous theories. Intriguingly, we discover that for large networks, if the performance of the machine learning model is slightly better than a random guess on the pairwise order of links, reliable restoration of the overall network formation process can be achieved. This suggests that evolution history restoration is generally highly feasible on empirical networks.

Date: 2024
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DOI: 10.1038/s41467-024-47248-x

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