Reconstructing missing complex networks against adversarial interventions.

Area of Science:

• Network science
• Causal inference
• Systems biology

Background:

• Complex network interactions dictate system behavior, but are often partially unobservable.
• Sensing limitations and adversarial actions hinder understanding of true network structures.

Purpose of the Study:

• To develop a general causal inference framework for reconstructing latent network structures under unknown adversarial interventions.
• To demonstrate the framework's applicability in diverse systems, including biological and social networks.

Main Methods:

• Proposed a novel causal inference framework to infer unobserved network structures.
• Applied the framework to reconstruct human protein-protein interaction networks and brain connectomes.
• Utilized simulated social network data with camouflage (removal processes) to test inference capabilities.

Main Results:

• Successfully recovered latent network structures in biological and social systems.
• Demonstrated high fidelity in inferring hidden subnetworks despite adversarial interventions.
• Validated the framework's effectiveness in uncovering concealed information.

Conclusions:

• The proposed causal inference framework is effective for reconstructing network structures under partial observability and adversarial conditions.
• The approach has broad applicability for capturing hidden information across various scientific domains.
• Enables deeper understanding of complex systems by revealing unobserved network components.