Gragnostics Rendering for Scalable Graph Classification
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Solution Overview
Problem
Conventional graph processing and rendering methods are inefficient and difficult to interpret for large graphs, especially in analytics environments, as they struggle with scalability and visual recognition due to the complexity of nodes and edges, leading to slow comparison processes and unintelligible results.
Innovation Solution
A graph processing system that computes a linearly computable feature vector based on graph statistics, allowing for scalable classification and visualization by projecting feature vectors onto a two-dimensional plane, enabling efficient comparison and interpretation of graph similarities and differences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional graph processing methods are used to handle large graphs, then complete graph data can be processed, but the processing becomes unwieldy and computationally expensive with exponential time complexity
Solution Approach 1:
The patent extracts only the essential structural features of graphs (node degrees, path lengths, clustering coefficients) to create feature vectors, rather than processing complete graph data. This extraction approach maintains the ability to compare and classify graphs while dramatically reducing computational complexity from exponential to polynomial time.
Solution Approach 2:
The patent transforms graph data from its original complex structure into simplified numerical parameters (feature vectors containing statistical measures). By changing the representation parameters from complete graph structures to condensed feature vectors, the system achieves scalable processing of large graph datasets.
2Quantity of substance
If large graphs with many nodes and vertices are visualized, then complete information is represented, but the visual output becomes amorphous and difficult to interpret
Solution Approach 1:
Instead of visualizing the complete graph structure directly, the patent creates a simplified copy in the form of feature vectors that represent the essential characteristics of graphs. These vector representations can be visualized in lower-dimensional spaces (e.g., 2D plots) while preserving the ability to distinguish and interpret different graph types.
Solution Approach 2:
The patent projects high-dimensional graph feature vectors into lower-dimensional visual spaces (such as 2D Cartesian planes) for visualization. This dimensionality reduction allows large graphs to be represented in visually interpretable forms while maintaining the ability to distinguish between different graph structures through their feature space positions.
3Measurement precision
If conventional graph comparison methods are used, then detailed graph analysis is possible, but the comparison process is slow and results are not expressive
Solution Approach 1:
The patent pre-computes and stores feature vectors for graphs, capturing essential structural characteristics in advance. When graph comparisons are needed, the system only needs to compare these pre-computed feature vectors rather than analyzing complete graph structures, significantly reducing comparison time while maintaining accuracy.
Solution Approach 2:
The patent changes the comparison parameters from complete graph structures to condensed feature vectors containing key structural statistics. This parameter transformation enables rapid comparison operations that are both faster and more expressive, as the feature vectors highlight the most distinguishing characteristics of graphs for comparison purposes.
Data Source
AI summary
A graph processing system, method and apparatus classifies graphs based on a linearly computable set of features defined as a feature vector adapted for comparison with the feature vectors of other graphs. The features result from graph statistics (“gragnostics”) computable from the edges and vertices of a set of graphs. Graphs are classified based on a multidimensional distance of the resulting feature vectors, and similar graphs are classified according to a distance, or nearest neighbor, of the feature vector corresponding to each graph. Projection of the feature vector onto two dimensions allows visualization of the classification, as similar graphs appear as clusters or groups separated by a relatively shorter distance. Different types or classifications of graphs also appear as other, more distant, clusters. An initial training set defines the classification types, and sampled graphs are evaluated and classified based on the feature vector and nearest neighbors in the training set.