Graph Neural Network Structure Learning from Noisy Node Features
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Solution Overview
Problem
Graph neural networks (GNNs) are limited in applicability due to the need for both node features and graph structure, which may not be available in real-world applications, leading to performance degradation when the graph structure is noisy or absent.
Innovation Solution
A system and method for generating a graph structure using self-supervision, termed SLAPS, which learns both GNN parameters and adjacency matrix simultaneously by masking or adding noise to input features, training a GNN to recover masked features, and updating the adjacency matrix to improve structure learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If graph neural networks require both node features and graph structure as input, then the model can capture both local and global information effectively, but the applicability is limited when graph structure is noisy or absent
Solution Approach 1:
The system uses self-supervision to learn graph structure from node features alone. The denoising autoencoder reconstructs noisy node features using the learned adjacency matrix, enabling the model to serve itself by generating the necessary structure information without external input
Solution Approach 2:
The patent introduces a denoising autoencoder as an intermediary component that bridges the gap between raw node features and the required graph structure. This intermediary learns to reconstruct features from noisy versions, effectively mediating between the availability of node features and the need for graph structure
2Adaptability or versatility
If the graph structure is noisy or incomplete, then the model can handle real-world data where perfect structure is unavailable, but the performance degrades
Solution Approach 1:
The patent converts the harmful effect of noise in node features into a beneficial training signal. By adding noise to node features and training a denoising autoencoder to reconstruct them, the system learns robust representations that actually improve performance on noisy real-world data
Solution Approach 2:
The system uses feedback from the denoising autoencoder to improve the adjacency matrix. The reconstructed node features provide feedback that guides the learning of better graph structure, creating a feedback loop that continuously refines both the structure and feature representations
3Measurement precision
If the system learns both GNN parameters and adjacency matrix simultaneously, then the model achieves better structure learning, but the computational complexity increases
Solution Approach 1:
The patent segments the learning process into two distinct parts: learning the adjacency matrix through the denoising autoencoder and learning GNN parameters separately. This segmentation allows each component to be optimized independently, reducing the overall computational complexity while maintaining high structure learning accuracy
Data Source
AI summary
A graph structure having nodes and edges is represented as an adjacency matrix, and nodes of the graph structure have node features. A computer-implemented method and system for generating a graph structure are provided, the method comprising: generating an adjacency matrix based on a plurality of node features; generating a plurality of noisy node features based on the plurality of node features; generating a plurality of denoised node features using a neural network based on the plurality of noisy node features and the adjacency matrix; and updating the adjacency matrix based on the plurality of denoised node features.