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Nat Commun. 2019 Apr 15;10(1):1738. doi: 10.1038/s41467-019-09774-x.

Reconstructing missing complex networks against adversarial interventions.

Author information

1
Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90007, USA.
2
Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90007, USA. pbogdan@usc.edu.

Abstract

Interactions within complex network components define their operational modes, collective behaviors and global functionality. Understanding the role of these interactions is limited by either sensing methodologies or intentional adversarial efforts that sabotage the network structure. To overcome the partial observability and infer with good fidelity the unobserved network structures (latent subnetworks that are not random samples of the full network), we propose a general causal inference framework for reconstructing network structures under unknown adversarial interventions. We explore its applicability in both biological and social systems to recover the latent structures of human protein complex interactions and brain connectomes, as well as to infer the camouflaged social network structure in a simulated removal process. The demonstrated effectiveness establishes its good potential for capturing hidden information in much broader research domains.

PMID:
30988308
PMCID:
PMC6465316
DOI:
10.1038/s41467-019-09774-x
[Indexed for MEDLINE]
Free PMC Article

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