Graph Neural Network Solver for Algorithmic Efficiency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional heuristic approaches for solving graph problems, such as identifying a maximal independent set, are inefficient and prone to error due to high computational resource requirements and reliance on discretionary human choices, which do not scale well with larger and more complex graphs.
Innovation Solution
A hybrid approach combining Deep Learning (DL) components and differentiable learnable graph embedding components, followed by post-processing, forms a pipeline that transforms graph inputs into vector representations, employs machine learning algorithms to generate probabilistic values, and uses a hindsight loss function to maintain solution diversity, thereby improving calculation efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional heuristic approaches are used to solve graph problems, then solutions can be derived with simple methods, but computational time and resource requirements increase significantly
Solution Approach 1:
The patent replaces traditional mechanical/heuristic computational approaches with a neural network-based system. The neural network learns optimal node selections through training on graph data, substituting manual heuristic methods with an automated learning system that reduces computational time while maintaining solution quality.
Solution Approach 2:
The patent performs preliminary training of the neural network on graph data before actual problem solving. This pre-learning phase enables the network to quickly process new graph problems without requiring extensive computational resources during the actual solving phase, effectively moving computational work to the training stage.
2Ease of manufacture
If traditional heuristic approaches are used, then implementation is straightforward, but accuracy and reliability of solutions decrease due to human discretion and errors
Solution Approach 1:
The patent replaces human heuristic judgment with a neural network system that processes graph data objectively. The network learns patterns from training data and applies them consistently to new problems, eliminating human errors and discretionary variations while maintaining ease of implementation through automated processing.
Solution Approach 2:
The neural network system is self-training and self-improving through the learning process. It automatically adjusts its parameters and decision-making logic based on training data, eliminating the need for manual tuning and human intervention while improving solution reliability through continuous optimization.
3Adaptability or versatility
If traditional approaches are applied to larger and more complex graphs, then comprehensive coverage is achieved, but computational resource requirements and time consumption increase exponentially
Solution Approach 1:
The patent implements a dynamic system where the neural network adapts its behavior based on the characteristics of the input graph. The network can handle varying graph sizes and complexities by leveraging patterns learned during training, maintaining computational efficiency across different problem scales without requiring exponential resources.
Solution Approach 2:
The patent changes the fundamental parameters of the problem-solving approach by transitioning from exhaustive search methods to neural network-based prediction. This parameter change enables the system to handle larger and more complex graphs efficiently by predicting solutions based on learned patterns rather than computing all possible configurations.
4Quantity of substance
If more computational resources are allocated to traditional heuristic methods, then solution completeness may improve, but the fundamental inefficiency and scaling problems persist
Solution Approach 1:
The patent substitutes resource-intensive traditional computational methods with a neural network system that requires fewer resources during operation. While training requires significant resources, the resulting model achieves high computational efficiency during deployment, fundamentally changing the resource-productivity tradeoff.
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture are disclosed to improve algorithmic solver performance. An example apparatus includes graph transforming circuitry to generate a vector representation corresponding to a graph input, vector classification circuitry to generate a node embedding machine learning classifier, the node embedding machine learning classifier to cause an output layer of probabilities corresponding to nodes of the graph input, loss calculating circuitry to train a model based on a target algorithmic function, the loss calculating circuitry to inject a solution diversity to reduce equivalent solution error of the target algorithmic function, and algorithmic solving circuitry to calculate solutions based on ranked ones of the output layer of probabilities.