Interactive Visual Graph Queries Using GNN Subgraph Matching
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Solution Overview
Problem
Existing graph pattern search algorithms are inefficient for interactive, large-scale graph databases, struggle with computational complexity, and lack accurate one-to-one node correspondence in subgraph matching, requiring substantial domain knowledge for refinement.
Innovation Solution
A system utilizing Graph Neural Networks (GNNs) for encoding graph topological and node attributes as fixed-length vectors, employing NeuroMatch for subgraph matching and NeuroAlign for node alignment, with a visual analytics interface for user interaction and result refinement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing graph pattern search algorithms are used on large-scale graph databases, then subgraph matching can be performed, but computational complexity becomes excessive and search efficiency deteriorates
Solution Approach 1:
The patent segments the graph matching problem into two distinct phases: (1) subgraph pattern matching to identify candidate graphs containing the query pattern, and (2) node correspondence alignment to establish accurate node mappings. This segmentation allows each phase to be optimized independently, reducing overall computational complexity while maintaining search efficiency.
Solution Approach 2:
The patent performs preliminary subgraph pattern matching to identify candidate graphs before conducting the more computationally intensive node correspondence alignment. By pre-filtering the search space to only graphs containing the query pattern, the system reduces the computational burden of subsequent alignment operations.
2Measurement precision
If existing graph pattern search algorithms are used, then subgraph matching can be performed, but accurate one-to-one node correspondence cannot be established
Solution Approach 1:
The patent separates subgraph pattern matching from node correspondence alignment into distinct processing stages. The first stage identifies candidate graphs efficiently using pattern matching, while the second stage establishes accurate node correspondences through specialized alignment algorithms, allowing each stage to be optimized for its specific goal.
Solution Approach 2:
The patent introduces an intermediate representation and alignment mechanism that bridges pattern matching results and final node correspondence. This intermediary alignment process transforms the rough matches from pattern searching into precise one-to-one node mappings without requiring complete re-analysis of the entire graph structure.
3Measurement precision
If domain knowledge refinement is applied to improve matching results, then accuracy improves, but system complexity and operational difficulty increase
Solution Approach 1:
The patent implements self-service mechanisms where the system automatically performs node correspondence alignment and result refinement without requiring manual domain knowledge input from users. The alignment algorithms autonomously establish accurate node mappings and the system automatically refines matching results, eliminating the need for users to possess specialized domain expertise.
Solution Approach 2:
The patent replaces manual domain knowledge-based refinement with automated computational alignment algorithms. Instead of relying on users to apply domain expertise to refine matches, the system uses neuroalign and other automated methods to establish accurate node correspondences, substituting mechanical human analysis with computational processes.
Data Source
AI summary
Systems and methods are disclosed for identifying target graphs that have nodes or neighborhoods of nodes (sub-graphs) that correspond with an input query graph. A visual analytics system supports human-in-the-loop, example-based subgraph pattern search utilizing a database of target graphs. Users can interactively select a pattern of nodes of interest. Graph neural networks encode topological and node attributes in a graph as fixed length latent vector representations such that subgraph matching can be performed in the latent space. Once matching target graphs are identified as corresponding to the query graph, one-to-one node correspondence between the query graph and the matching target graphs.