Entity-Relationship Embeddings for Social Graph Moderation
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Solution Overview
Problem
Social networks face challenges in managing diverse types of entities and relationships within their social graphs, which are typically one-dimensional and focused on homogeneous relationships, lacking the ability to effectively understand and utilize information about relationships between different entity types.
Innovation Solution
The technology embeds diverse entities into embedding spaces based on their relationships with other entity types, using machine learning models to analyze these spaces and identify characteristics such as inappropriate content or community violations, thereby improving moderation and user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If social networks use traditional one-dimensional social graphs with homogeneous relationships, then the system structure is simple and easy to manage, but the system cannot effectively understand and utilize information about relationships between different entity types
Solution Approach 1:
The patent transforms the traditional one-dimensional social graph into a multi-dimensional embedding space where entities of different types (users, posts, comments, etc.) can be represented and their relationships captured across multiple dimensions. This dimensional expansion allows the system to preserve rich relationship information while maintaining computational efficiency through vector representations.
Solution Approach 2:
The patent changes the representation parameters of entities from discrete categorical values to continuous embedding vectors. This parameter transformation enables the system to capture nuanced relationships between different entity types by positioning similar entities closer together in the embedding space, thereby preserving relationship information without requiring complex graph structures.
2Measurement precision
If social networks process raw data repeatedly for moderation and analysis, then accurate identification of issues is achieved, but computational resources are wasted and processing time increases
Solution Approach 1:
The patent performs preliminary action by pre-processing entity data into embedding representations that capture essential relationship information. These pre-computed embeddings serve as efficient inputs for subsequent moderation and analysis tasks, eliminating the need to repeatedly process raw data while maintaining high identification accuracy.
Solution Approach 2:
The patent creates compact embedding vector copies of entity data that preserve the essential information needed for moderation and analysis. Instead of repeatedly processing the full raw data, the system uses these compressed embedding representations, which significantly reduce computational overhead and processing time while maintaining measurement precision.
3Reliability
If social networks create comprehensive embeddings of diverse entities, then better entity representation and moderation are achieved, but the complexity of creating and managing multiple embedding spaces increases
Solution Approach 1:
The patent implements a universal embedding framework where a single embedding space can represent multiple entity types (users, posts, comments, groups, etc.) simultaneously. This multi-functional approach allows the system to achieve reliable moderation across different entity types while avoiding the complexity of managing separate embedding spaces for each entity type.
Data Source
AI summary
The present technology utilizes data about diverse types of entities to inform machine learning models. The present technology can ingest social network data that includes nodes from diverse entity types, and utilize information implicit in relationships between one entity type with another entity type. The present technology creates a plurality of embedding spaces for representing entities of different types as vectors. The vectors identify a first entity, a second entity, and a relationship between the entities. The data from the embedding spaces can be input into a model configured to classify entities as likely to be associated with an explored characteristic, and output of a classification of entities that are likely associated with the explored characteristic. The model is configured to relate the different types of entities to classify entities as likely to be associated with the explored characteristic.