Vector Embeddings for Dynamic Social Network Taxonomy
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Solution Overview
Problem
Taxonomy data in social networks struggles to capture dynamic and subtle relationships between entities, as existing hierarchical structures are static and fail to account for varying analysis requirements, limiting their usefulness in predictive tasks.
Innovation Solution
A deep representation of social network entities is created using machine learning techniques, where entities are mapped to vector representations, allowing for optimized embeddings that capture complex relationships and enable advanced analyses such as clustering, similarity identification, and prediction tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If entities are organized using standardized hierarchical taxonomies, then data structure and categorization are improved, but the ability to capture dynamic and subtle relationships between entities deteriorates
Solution Approach 1:
The patent transitions from traditional hierarchical taxonomy (single dimension) to a vector space representation (multi-dimensional space). Each entity is mapped to a vector that captures multiple attributes and relationships simultaneously, allowing dynamic relationships to be expressed through vector operations while maintaining the stability of the underlying taxonomy structure.
Solution Approach 2:
The patent changes the parameter representation from discrete categorical values in a hierarchy to continuous vector embeddings. This allows relationships to be captured through continuous parameter variations in the vector space, enabling subtle and dynamic relationships to be represented while the taxonomy provides stable categorical grounding.
2Ease of manufacture
If traditional hierarchical taxonomies are used, then ease of implementation is improved, but predictive accuracy for analysis tasks deteriorates
Solution Approach 1:
The patent performs preliminary action by pre-computing vector embeddings for all taxonomy entities and storing them in advance. This allows the system to maintain simple taxonomy implementation while having predictive-capable representations ready for analysis tasks, eliminating the need for complex real-time computations during prediction.
Solution Approach 2:
The patent introduces vector embeddings as an intermediary between the simple hierarchical taxonomy and the predictive analysis tasks. The embeddings serve as a bridge that translates the structured taxonomy data into a form suitable for machine learning and predictive operations, maintaining ease of taxonomy implementation while enabling high predictive accuracy.
3Device complexity
If static taxonomy relationships are maintained, then system simplicity is improved, but the ability to capture dynamic relationships deteriorates
Solution Approach 1:
The patent introduces dynamics into the taxonomy system by representing entity relationships through vector embeddings that can capture temporal and contextual variations. While the taxonomy hierarchy remains static and simple, the vector representations allow dynamic relationships to be expressed through operations on the embeddings, such as measuring similarity or computing relationships in vector space.
Data Source
AI summary
In an example embodiment, for each of a plurality of different entities in a social network structure, the entity is mapped into a vector having n coordinates. The vector for each of the plurality of different entities is stored in a deep representation data structure. One or more objective functions are applied to at least one combination of two or more of the vectors in the deep representation data structure. Then, an optimization test on each of the at least one combination of two or more of the vectors is performed using a corresponding objective function output for each of the at least one combination of two or more of the vectors, and, for any combination that did not pass the optimization test, one or more coordinates for the vectors in the combination are altered so that the vectors in the combination become closer together within an n-dimensional space.