Title Similarity via Transition Graphs in Social Networks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems face challenges in determining similar locations and titles based on transition data in social networking services, as common metrics like population size and weather may not accurately reflect industry-specific similarities, leading to inadequate insights for users.
Innovation Solution
A method is implemented to construct directed weighted graphs for locations and titles, using transition data and Laplacian smoothing to determine similar locations and titles, which are then used to provide statistical insights to users, incorporating personalized PageRank algorithms and weighted combinations across different granularities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If common metrics like population size and weather are used to determine location similarity, then the calculation is simple and fast, but the accuracy of industry-specific similarities is poor
Solution Approach 1:
The patent transforms the similarity measurement from using static demographic parameters (population, weather) to dynamic transition-based parameters (job movement patterns). By changing the fundamental parameters used for comparison to industry-specific transition data, the system achieves accurate industry-relevant location similarity while maintaining computational efficiency through graph-based algorithms.
Solution Approach 2:
The patent introduces transition data as an intermediary layer between locations and similarity metrics. Instead of directly comparing location attributes, the system uses job transition patterns as a mediator to infer location similarity, capturing industry-specific relationships that traditional metrics miss.
2Loss of information
If transition data from all granularities is used to compute similar titles, then the comprehensiveness of insights is improved, but the computational load increases
Solution Approach 1:
The patent segments title similarity computation into multiple granularity levels (individual titles, title families, broad categories). By dividing the computation hierarchy, the system can efficiently process data at each level and combine results, maintaining comprehensive insights while reducing overall computational complexity through hierarchical organization.
Solution Approach 2:
The patent implements dynamic computation where the granularity level adapts based on query context and data availability. The system can dynamically adjust between fine-grained individual title comparisons and coarse-grained category-level analysis, optimizing computational power usage while preserving insight completeness through context-aware granularity selection.
Data Source
AI summary
In an example, a granularity of title similarity is determined, wherein the granularity of title similarity is a level at which social networking data should be filtered to identify titles similar to a target title. Then a weighted graph of titles is constructed at the granularity of title similarity, wherein each node in the weighted graph is a title and a directed edge exists in the weighted graph between a first node and a second node if the social networking data, at the granularity of title similarity, indicates that a transition occurred wherein a member who held a position with a title corresponding to the first node transitioned to a position with a title corresponding to the second node, wherein each directed edge contains a weight indicating a strength of relationship between nodes. The weighted graph of locations is traversed from a node corresponding to the target title in order to identify titles similar to the target title.