Graph Neural Network Training With Universal Adversarial Perturbations
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Solution Overview
Problem
Graph neural networks (GNNs) are vulnerable to perturbations, leading to reduced performance and requiring additional resources for training and testing, which can be mitigated by generating robust GNNs using universal adversarial training.
Innovation Solution
A method involving universal adversarial training is employed, where model parameters are initialized based on Bayesian Personalized Ranking (BPR) loss, perturbation parameters are randomly initialized, and a complementary graph is determined, with stochastic gradient ascent and descent updates to generate a universal perturbation matrix, enhancing resilience to perturbations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If additional layers of training, ensemble analysis, or post-generation testing are applied to compensate for perturbation vulnerability, then performance reliability is improved, but computational resource requirements and system complexity increase
Solution Approach 1:
The patent applies preliminary action by incorporating adversarial training during the model generation phase rather than adding remedial layers later. The GNN model is trained with perturbed graphs and universal adversarial perturbations from the beginning, building robustness into the fundamental structure. This prevents the need for additional post-generation testing or ensemble methods, as the model inherently resists perturbations through pre-trained adversarial awareness.
Solution Approach 2:
The patent converts the harmful effect of perturbations into a beneficial training mechanism. By intentionally introducing universal adversarial perturbations during training, the model learns to recognize and resist various graph perturbations. The harmful perturbations become a teaching tool that strengthens the model's robustness, transforming what would normally degrade performance into a mechanism that improves reliability without adding system complexity.
2Reliability
If GNNs are trained to be robust against perturbations through additional training layers or ensemble methods, then resilience to perturbations is improved, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary adversarial training during model generation, embedding perturbation resistance into the base model structure. This upfront action eliminates the need for time-consuming post-generation testing and multiple training layers, as robustness is built-in from the start rather than added as a time-intensive remedial measure.
Solution Approach 2:
The patent employs universal adversarial perturbations that can defend against multiple types of graph perturbations simultaneously. A single universal perturbation matrix trained during model generation provides protection against various edge additions, deletions, and modifications. This multi-functional approach achieves broad perturbation resilience through one training process rather than requiring separate training layers for each perturbation type, thereby reducing overall processing time.
3Measurement precision
If GNN models are made robust through ensemble analysis or additional training, then accuracy under perturbation is improved, but computational resources and memory storage requirements increase
Solution Approach 1:
The patent converts the computational cost of dealing with perturbations into a beneficial pre-training investment. By spending computational resources once during model generation to learn universal adversarial perturbations, the system achieves sustained accuracy under perturbation without requiring ongoing ensemble analysis or additional training. The initial computational expenditure creates a efficiently robust model that maintains accuracy with minimal additional resource consumption.
Solution Approach 2:
The patent enables the GNN model to serve itself by building intrinsic perturbation resistance through adversarial training. The model learns to identify and counteract perturbations through its own trained parameters rather than requiring external ensemble methods or additional processing resources. This self-service capability maintains accuracy under perturbation using the model's inherent learned robustness, eliminating the need for resource-intensive external assistance mechanisms.
Data Source
AI summary
Described are a method, system, and computer program product for generating robust graph neural networks using universal adversarial training. The method includes receiving a graph neural network (GNN) model and a bipartite graph including an adjacency matrix, initializing model parameters of the GNN model, initializing perturbation parameters, and sampling a subgraph of a complementary graph based on the bipartite graph. The method further includes repeating until convergence of the model parameters: drawing a random variable from a uniform distribution; generating a universal perturbation matrix based on the subgraph, the random variable, and the perturbation parameters; determining Bayesian Personalized Ranking (BPR) loss by inputting the bipartite graph and the universal perturbation matrix to the GNN model; updating the perturbation parameters based on stochastic gradient ascent; and updating the model parameters based on stochastic gradient descent. The method further includes, in response to convergence of the model parameters, outputting the model parameters.