Wang, J., Zhang, Y.-J., Xu, C., Li, J., Sun, J., Xie, J., Feng, L., Zhou, T., & Hu, Y. (2024). Reconstructing the evolution history of networked complex systems. Nature Communications, 15(1). https://doi.org/10.1038/s41467-024-47248-x
Abstract:
AbstractThe 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.
License type:
Attribution 4.0 International (CC BY 4.0)
Funding Info:
This research / project is supported by the Ministry of Communications and Information - Online Trust and Safety (OTS) Research Programme
Grant Reference no. : MCI-OTS-001
This research / project is supported by the Ministry of Education - Academic Research Fund (AcRF) Tier 1 administered by NUS
Grant Reference no. : A-0004550-00-00