Degenerated Network Generation for Graph SSL Noise Differentiation
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Solution Overview
Problem
Conventional graph self-supervised learning (SSL) techniques face challenges in effectively differentiating noise links and missing links in graph data, leading to inadequate data augmentation and pretext task performance.
Innovation Solution
A device and method for generating alternative networks with similar complex network parameters but different microscopic structures, using techniques such as perturbation methods and Monte-Carlo processes to create degenerated networks that can help differentiate noise and missing links, and improve SSL performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional graph SSL augmentation techniques (node/link dropout, feature masking) are used, then data augmentation is achieved, but the techniques are indirect and do not sufficiently consider context
Solution Approach 1:
The patent changes the parameters of graph augmentation by introducing degree distribution constraints and complex network properties. Instead of simple random dropout, the invention generates alternative graphs that preserve specific network parameters (degree distribution, clustering coefficient, average path length) while varying local structures. This allows context-aware augmentation that maintains the essential topological characteristics of the original graph.
Solution Approach 2:
The patent performs preliminary analysis of the original graph's complex network parameters before generating alternative graphs. By pre-calculating degree distributions, clustering coefficients, and other topological metrics, the invention can guide the augmentation process to preserve these characteristics, ensuring context consideration before the actual augmentation occurs.
2Device complexity
If simple augmentation techniques are used, then computational complexity is low, but noise links and missing links cannot be differentiated
Solution Approach 1:
The patent implements a feedback mechanism where the generated alternative graphs are evaluated against the original graph's complex network parameters. The degree distribution, clustering coefficient, and other metrics serve as feedback signals to guide the augmentation process, allowing the system to iteratively refine the alternative graphs until they meet the desired parameter constraints, thereby enabling noise link differentiation.
Solution Approach 2:
The invention introduces dynamic graph generation where alternative graphs are created with varying degrees of perturbation. By controlling the extent of structural changes while maintaining parameter constraints, the system can dynamically adjust the augmentation intensity to differentiate noise links from meaningful connections without excessive computational complexity.
3Ease of manufacture
If existing graph SSL methods are used, then implementation is simple, but missing links or unobserved links cannot be identified
Solution Approach 1:
The patent creates multiple copied versions of the original graph with systematic variations. By generating alternative graphs that preserve global topological properties but differ in local connections, the invention can compare these copies to identify consistent patterns that likely represent true connections versus random noise or missing links, thereby recovering lost information without complex implementation.
Data Source
AI summary
Provided are a device and method for generating a network using complex network properties. According to the present invention, by introducing knowledge of a complex network and generating a plurality of substitutable degenerated networks as an ensemble to statistically process noise and missing links, it is possible to generate graph instances and data from which intrinsic defects of original data are removed. A device for generating a network according to the present invention includes an original network construction unit configured to construct an original network for data received from the outside, a complex network parameter extraction unit configured to construct a parameter set with complex network parameters extracted from the original network, and a degenerated network generation unit configured to generate a degenerated network that satisfies the parameter set within a predetermined error range.