Graph Similarity Evaluation Using Dual Encoder Architecture
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Solution Overview
Problem
Current machine learning methods face challenges in evaluating graph similarity due to the lack of structured data constraints, requiring models to learn both task-specific and structural representations of graphs, which is complex and often unsupervised.
Innovation Solution
The proposed solution involves training source and target graph encoders using attention models and reverse attention models to generate embeddings for graph similarity comparison, allowing for unsupervised learning and high-level insights across various technical fields.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If unsupervised learning is used to evaluate graph similarity, then the model can handle free-form graph structures without predefined constraints, but the complexity of learning both task-specific and structural representations increases significantly
Solution Approach 1:
The patent divides the complex graph encoding task into two separate encoder components: a structural encoder that processes graph topology and a content encoder that processes node/edge attributes. This segmentation allows each encoder to specialize in one aspect, reducing the overall complexity while maintaining adaptability to various graph structures and tasks.
Solution Approach 2:
The patent designs universal encoder architectures that can handle multiple graph types and tasks simultaneously. The structural encoder and content encoder are formulated as general frameworks that can process different graph structures (trees, grids, irregular graphs) and serve multiple purposes (classification, regression, similarity evaluation) without requiring task-specific customization.
2Measurement precision
If graph encoders are trained to predict graph characteristics, then the model can capture structural information, but the training process requires multiple encoders and attention mechanisms increasing computational complexity
Solution Approach 1:
The patent introduces attention mechanisms as intermediary components that bridge the structural encoder and content encoder. The attention mechanism selectively weights and integrates information from both encoders, enabling accurate graph characteristic prediction by focusing on relevant structural and content features while managing the complexity of combining multiple encoder outputs.
Solution Approach 2:
The patent implements a nested encoder architecture where the structural encoder and content encoder are embedded within a unified framework. The encoders are nested within attention mechanisms and loss function compositions, creating a hierarchical structure that organizes complexity systematically and enables efficient training through modular optimization.
Data Source
AI summary
The present disclosure provides computing systems and methods directed to algorithms and the underlying machine learning (ML) models for evaluating similarity between graphs using graph structures and/or attributes. The systems and methods disclosed may provide advantages or improvements for comparing graphs without additional context or input from a person (e.g., the methods are unsupervised). In particular, the systems and methods of the present disclosure can operate to generate respective embeddings for one or more target graphs, where the embedding for each target graph is indicative of a respective similarity of such target graph to each of a set of source graphs, and where a pair of embeddings for a pair of target graphs can be used to assess a similarity between the pair of target graphs.