Autoregressive Graph Generation via Hierarchical Embeddings
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Solution Overview
Problem
Current machine learning models face challenges in efficiently generating and training on large graphs, particularly due to high computational resource requirements and synchronization issues, which limits their ability to handle graphs with a large number of nodes.
Innovation Solution
A graph generation system that uses a hierarchical arrangement of embeddings to autoregressively generate edge sets for each node, allowing for efficient integration of information and parallelization during training, by constructing an edge tree conditioned on context embeddings and maintaining a hierarchy of embeddings to reduce computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current machine learning models are used to generate and train on large graphs, then graph generation capability is achieved, but computational resource requirements become excessively high
Solution Approach 1:
The patent segments the graph generation process into independent node-level operations. Each node's edge set is generated separately using autoregressive modeling, allowing the overall graph generation task to be divided into manageable, parallelizable units. This segmentation enables efficient processing of large graphs by reducing the computational burden on any single processing unit while maintaining the ability to generate coherent graph structures across the entire graph.
2Productivity
If current machine learning models are used to generate and train on large graphs, then graph generation capability is achieved, but synchronization issues arise during training
Solution Approach 1:
By segmenting the graph into nodes with independent edge set generation processes, the patent eliminates synchronization dependencies between different parts of the graph during training. Each node can be processed independently based on its contextual embedding, allowing for parallel training without requiring complex synchronization mechanisms between processing units.
Solution Approach 2:
The patent performs preliminary computation of contextual embeddings for each node before generating edge sets. This preliminary action provides each node with the necessary contextual information from previously processed nodes, enabling independent and consistent processing without requiring synchronization during the actual edge set generation and training phases.
3Productivity
If hierarchical arrangement of embeddings is used to autoregressively generate edge sets, then information integration efficiency is improved, but model complexity increases
Solution Approach 1:
The patent introduces a hierarchical dimension to the embedding structure, organizing embeddings across multiple levels to capture different scopes of contextual information. This dimensional organization allows efficient integration of information from various parts of the graph by systematically aggregating embeddings from lower to higher levels, improving information integration while maintaining a structured and manageable model architecture.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating data defining a graph. In one aspect, a method comprises: sequentially generating a respective edge set for each node in the graph, wherein for each of a plurality of nodes after a first node, generating the edge set for the node comprises: receiving a context embedding for the node that summarizes a respective edge set for each node that precedes the node; generating, based on the context embedding for the node: (i) a respective edge set for the node, and (ii) a respective embedding of the edge set for the node; generating a context embedding for a next node in the ordering of the nodes using the embedding of the edge set for the node; and adding the set of edges defined by the edge set for the node to the graph.