Graph Embedding Integration via Irrational Value Reflection
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Solution Overview
Problem
Existing graph embedding methods face challenges in integrating different embedding representations without loss of information or expressive power, leading to increased complexity in downstream tasks due to the concatenation of embedding vectors, which can result in cancellation of information during aggregation.
Innovation Solution
A graph embedding method that acquires multiple embedding representations, reflects a specific value, such as an irrational number, into one of the representations to prevent cancellation, and aggregates them using operations like multiplication and addition, allowing for integration without information loss and maintaining expressive power, even with varying representation sizes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If embedding vectors are concatenated together to integrate individual embedding representations, then information loss is prevented, but the complexity of downstream tasks increases proportionally to the number of embedding vectors
Solution Approach 1:
The patent transforms the integration operation from concatenation (which increases dimensionality) to element-wise multiplication followed by summation. This changes the parameter space from growing with the number of embeddings to a fixed dimension, resolving the contradiction between preserving information and maintaining task complexity.
Solution Approach 2:
The patent introduces an intermediary transformation step where each embedding vector is multiplied by a specific value (such as an irrational number) before aggregation. This intermediary operation prevents information cancellation during integration while maintaining a compact representation, thus preventing both information loss and complexity increase.
2Adaptability or versatility
If multiple embedding representations are integrated without transformation, then diverse graph information is captured, but information cancellation occurs during aggregation
Solution Approach 1:
The patent applies preliminary anti-action by multiplying each embedding representation by a specific value (such as an irrational number) before aggregation. This pre-transformation ensures that when embeddings are summed, their contributions do not cancel each other out, thus preserving the diverse graph information while preventing information loss.
3Adaptability or versatility
If embedding vectors of different sizes are integrated, then comprehensive graph representations are achieved, but resizing operations are required
Solution Approach 1:
The patent changes the integration paradigm from dimension-matching concatenation to element-wise operations that naturally handle different sizes. By using multiplication and summation instead of concatenation, embeddings of different dimensions can be integrated without requiring resizing operations, thus achieving comprehensive representations while maintaining operational simplicity.
Data Source
AI summary
A graph embedding method and system thereof are provided. The graph embedding method according to some embodiments includes acquiring a first embedding representation and a second embedding representation of a target graph; changing the second embedding representation by reflecting a specific value into the second embedding representation; and generating an integrated embedding representation by aggregating the first embedding representation and the changed second embedding representation.