View-Independent Node Embeddings for Multi-Graph Analysis
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Solution Overview
Problem
As the complexity of electronic content on the Internet increases, identifying relationships between this content using multi-view graphs becomes challenging due to the complexity in defining edges between nodes in a particular view, which affects node embedding and analysis.
Innovation Solution
The method involves determining node embedding using multi-view graphs by retrieving electronic content, identifying nodes and views, generating sequences of nodes, and calculating view-independent embeddings to preserve information across different views, allowing for richer signal utilization and higher quality embedding results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multi-view graphs are used to identify relationships between electronic content, then the ability to analyze complex content relationships is improved, but the complexity in defining edges between nodes increases
Solution Approach 1:
The patent introduces view-independent node embeddings as an intermediary representation that mediates between multiple views. These embeddings capture relationships across different views without requiring explicit edge definitions between all node pairs, thus reducing the complexity of defining edges while maintaining the ability to analyze complex content relationships.
Solution Approach 2:
The patent transforms the problem from defining explicit edges between nodes to learning latent embedding vectors for each node. By changing the parameter representation from discrete edge definitions to continuous embedding vectors, the system can capture complex relationships more efficiently with lower complexity in the graph structure.
2Measurement precision
If view-specific embeddings are used for each node in different views, then the semantic meaning of each view is preserved, but information loss occurs across different views
Solution Approach 1:
The patent merges multiple view-specific embeddings into a unified view-independent embedding for each node. This is achieved by learning a shared embedding space where nodes from different views are represented in a common vector space, thereby preserving information across views while maintaining the semantic characteristics of each individual view through the aggregation process.
Data Source
AI summary
Embodiments of the present disclosure relate generally to determining node embedding using multi-view graphs for analyzing electronic content.