Social Graph Analytics for Talent Retention Policy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Cities face challenges in retaining talented individuals and developing industries corresponding to their talents, as skilled workers often migrate to areas like Silicon Valley, making it difficult for city and state planners to understand the economic landscape and formulate retention policies.
Innovation Solution
A system and method that utilize a graph analytic engine and framework to determine economic graph indices by extracting attributes from member profiles on social networking platforms, constructing graphs, and visualizing correlations between skills, geographies, and industries, allowing for the assignment of values to nodes and propagation of these values to provide insights into talent distribution and industry development.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If social networking services are used to track member profiles and relationships, then insights into talent distribution and industry development can be obtained, but privacy concerns and data security risks increase
Solution Approach 1:
The patent extracts specific attributes (skills, geographies, industries) from member profiles to construct economic graphs, separating the useful analytical information from the broader personal data. This allows insights to be generated without requiring access to complete personal profiles, thereby reducing privacy concerns while maintaining analytical value.
Solution Approach 2:
The patent introduces an intermediary processing layer that transforms raw member profile data into aggregated economic graph representations. This intermediary layer aggregates and anonymizes data before analysis, preventing direct exposure of individual member information while preserving trend and pattern insights.
2Loss of information
If graph analytic engines process large amounts of member profile data, then comprehensive economic insights can be generated, but computational resources and processing time increase
Solution Approach 1:
The patent extracts only the necessary attributes (skills, geographies, industries) from member profiles rather than processing complete profiles. This selective extraction significantly reduces the volume of data requiring analysis while maintaining the ability to generate comprehensive economic insights from the extracted features.
Solution Approach 2:
The patent segments the analysis into distinct graph construction phases: extracting attributes, constructing graphs from extracted data, and performing analysis on the graphs. This segmentation allows for optimized processing at each stage and enables parallel computation of graph construction and analysis operations.
3Productivity
If cities do not have industrial bases matching talent production, then talented individuals migrate to areas like Silicon Valley, but city planners lack the data to formulate effective retention policies
Solution Approach 1:
The patent performs preliminary construction of economic graphs by extracting and organizing member profile attributes into structured graph representations before analysis. This preliminary structuring enables city planners to access and interpret economic landscape data more effectively, allowing them to formulate targeted retention policies based on visualized talent distribution and industry correlations.
Data Source
AI summary
This disclosure is directed to determining various economic graph indices and, in particular, to systems and methods that leverage a graph analytic engine and framework to determine values assigned to graph nodes extracted from one or more member profiles, and visualizing said values to correlate skills, geographies, and industries. The disclosed embodiments include a client-server architecture where a social networking server has access to a social graph of its social networking members. The social networking server includes various modules and engines that import the member profiles and then extracts certain defined attributes from the member profiles, such as employer (e.g., current employer and/or past employers), identified skills, educational institutions attended, and other such defined attributes. Using these attributes as nodes, the social networking server constructs a graph using various graph processing techniques. The resulting graph is then used to correlate and rank the various attributes that define the graph.