Synthetic Network Generator for Covert Analytics
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Solution Overview
Problem
Covert networks, such as terrorist and criminal networks, often hide their membership, structure, and activities, leading to incomplete and inaccurate data, making it challenging for researchers to interpret their structures and dynamics, and such data may be inaccessible due to privacy constraints.
Innovation Solution
A synthetic network generator system that receives anonymized input data, determines edge probabilities, and generates statistically similar synthetic networks using techniques like Stochastic block models and weighted random graph methods to create alternative interpretations of the original network, supporting analysis with partial information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If actual data about covert networks is collected, then the completeness and accuracy of network information is improved, but the accessibility of data to researchers deteriorates due to privacy constraints and security concerns
Solution Approach 1:
The patent creates synthetic network data that replicates the structural and functional characteristics of actual covert networks without using real sensitive information. The synthetic data generation system produces artificial network representations that maintain statistical properties, community structures, and interaction patterns of real networks while being completely anonymized and accessible for research purposes.
2Ease of operation
If anonymization techniques are applied to protect privacy, then data accessibility is improved, but the completeness and accuracy of network information deteriorates
Solution Approach 1:
The patent transforms network data by changing parameters such as node identifiers, edge weights, and community labels while preserving the underlying network structure and statistical properties. The anonymization process applies parameter transformations that protect individual identities and sensitive attributes while maintaining the overall network characteristics necessary for research analysis.
Solution Approach 2:
The patent applies different levels of anonymization and detail preservation to different parts of the network data. Sensitive local information such as individual node identities and specific edge attributes are anonymized, while global structural properties such as community structures, degree distributions, and path lengths are preserved to maintain data completeness for research purposes.
3Reliability
If multiple synthetic networks are generated, then the reliability of analytical results is improved through statistical validation, but the computational resources and time required deteriorates
Solution Approach 1:
The patent generates a controlled number of synthetic networks (e.g., 10-100 replicates) rather than exhaustive sampling, which provides sufficient statistical power for reliability assessment while limiting computational burden. The system performs partial replication necessary for validating analytical results without generating excessive synthetic data that would waste computational resources.
Solution Approach 2:
The patent performs preliminary analysis of the input network to identify key structural properties, community structures, and statistical parameters before generating synthetic networks. This preliminary characterization guides the synthetic data generation process to focus computational resources on preserving the most critical network features, reducing overall computation time while maintaining result reliability.
Data Source
AI summary
A method of generating a synthetic network includes receiving, by a group structure identification module, anonymized input data related to an original network. The anonymized input data includes an anonymized list of nodes, a list of edges and a list of groups. The method further includes determining, by the group structure identification module, for each pair of nodes, a probability of an edge between the pair of nodes. A resulting list of probabilities corresponds to a summary group structure. The method further includes generating, by a synthetic random network generation module, at least one synthetic random network based, at least in part, on the determined probabilities.