Computational Link Analysis System for Latent Relationship Discovery
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Solution Overview
Problem
Current computational link analysis methods are inefficient and impractical due to the need for manual processes, lack of computational support, and difficulty in capturing and analyzing large amounts of data to identify latent relationships in human networks.
Innovation Solution
A method and system for improving computational link analysis efficiency by organizing data into a link-oriented dataset with typed nodes and edges, and using synthesized edges derived through link derivation and induction to reveal associations not explicitly expressed in the data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual link analysis processes are used, then analysts can identify relationships in human networks, but the process is highly subjective, time-consuming, and difficult to reproduce
Solution Approach 1:
The patent replaces manual mechanical analysis processes with computational algorithms. Automated link analysis software processes network data using standardized algorithms, eliminating subjective human judgment while maintaining analytical accuracy. The system computes relationships between entities automatically, reducing both time consumption and subjectivity in the analysis process.
Solution Approach 2:
The patent transforms unstructured network data into standardized parameters and metrics that can be computationally processed. By defining specific parameters for relationship strength, connection types, and network patterns, the system enables automated analysis while preserving the nuanced insights that manual analysis provided.
2Productivity
If computational tools are introduced to support link analysis, then productivity increases, but the tools lack integration and require ad hoc processes
Solution Approach 1:
The patent merges multiple disconnected analytical tools into a single integrated platform. The system combines data collection, network construction, relationship analysis, and visualization functions into one cohesive toolset, eliminating the need for analysts to assemble and coordinate multiple separate tools while maintaining high productivity.
Solution Approach 2:
The patent creates a universal analysis platform that handles multiple types of network data and analysis tasks through a single system. The tool can process various data formats, apply different analytical methods, and produce multiple output types, replacing the need for specialized ad hoc processes for each analytical task.
3Reliability
If standardized intelligence practices are implemented, then reproducibility improves, but the practices become brittle when tools change
Solution Approach 1:
The patent implements dynamic, configurable analysis pipelines that can adapt to different tools and data sources. The system uses parameterized workflows that can be adjusted without breaking standardized practices, allowing reproducibility to be maintained while accommodating tool changes through flexible configuration rather than rigid hardcoding.
4Adaptability or versatility
If analysts use disconnected tools for information discovery, then various analysis tasks can be performed, but the process becomes difficult to use uniformly and requires manual coordination
Solution Approach 1:
The patent performs preliminary organization and structuring of network data before analysis tasks are executed. The system pre-processes data into standardized formats, pre-defines relationship types, and pre-configures analysis parameters, which simplifies subsequent analytical operations and improves ease of use without limiting analytical capabilities.
Data Source
AI summary
Observable data points are collected and organized into a link-oriented data set comprising nodes and links. Information is abstracted for use in link analysis by generating links between the collected data points, including deriving links and inducing links. A link can be induced by linking together a pair of nodes that satisfy a distance function. Exemplary distance functions that can be used to induce links include geospatial proximity, attribute nearness, and name similarity. Paths can be identified between selected nodes of interest through a dataset operation, and nodes and/or links can be selectively included or excluded from the data set operation. The dataset can be augmented with pedigree information or one or more association nodes. Link information, including a trajectory and a connected path that selectively produces or excludes one or more intermediate nodes, can be displayed and/or produced in a specified format.