Graph Pattern Processing for Multi-Relational GNN Classification
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Solution Overview
Problem
Existing technologies lack efficient mechanisms to manage, integrate, and analyze ever larger graphs, particularly in domains like biology, finance, or politics, where complex concepts and relationships are encoded in graph structures, necessitating improved processing and classification.
Innovation Solution
The approach transforms a homogenous graph into a multi-relational representation by identifying subgraphs of predefined patterns, using a multi-relational graph neural network (RGCN) to generate embeddings or classifications based on domain-specific ontologies, leveraging patterns to enhance data analysis and classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing technologies are used to process large graphs, then processing capability is limited, but system complexity increases due to lack of efficient mechanisms
Solution Approach 1:
The patent segments the large graph into multiple subgraphs based on predefined patterns. Each subgraph represents a specific pattern instance (e.g., transaction patterns, social network patterns), allowing the system to process complex graphs by breaking them down into manageable, pattern-based components that can be analyzed independently and then integrated.
Solution Approach 2:
The patent transforms the graph representation by changing parameters from a homogeneous structure to a multi-relational structure with pattern-specific attributes. This involves encoding pattern types, pattern instances, and relationship types as explicit parameters, enabling more efficient processing through parameterized pattern matching and reducing the need for complex general-purpose graph algorithms.
2Measurement precision
If multi-relational representations are used, then classification accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent performs preliminary action by pre-defining patterns and their relationships before processing the actual graph data. The system establishes a pattern schema that specifies valid graph patterns, relationships, and ontologies in advance, allowing the multi-relational representation to be constructed systematically rather than requiring complex real-time analysis during classification.
Solution Approach 2:
The patent introduces pattern instances as intermediary elements between the raw graph data and the classification process. These pattern instances serve as mediators that capture specific relationship patterns (e.g., money laundering patterns, social network patterns) and translate them into structured representations that the classification algorithm can process efficiently, reducing the direct complexity of analyzing raw graph relationships.
3Adaptability or versatility
If graph structures are used to encode complex concepts, then data representation capability improves, but analysis efficiency decreases
Solution Approach 1:
The patent applies local quality by assigning pattern-specific properties and relationships to different parts of the graph based on their local structure. Instead of treating the entire graph uniformly, the system identifies and marks specific subgraphs that match predefined patterns (e.g., cycle patterns, star patterns, path patterns), allowing analysis to focus on locally relevant pattern instances rather than processing the entire graph structure globally.
Data Source
AI summary
In an approach, a processor identifies subgraphs of predefined patterns in a first graph, the first graph: (i) representing a specific ontology and (ii) comprising nodes representing first entities and first edges representing relationships between the first entities. A processor represents the identified subgraphs by respective second graphs, thereby forming multi-relational graphs, each second graph comprising nodes representing second entities and second edges representing relationships between the second entities, where: the second entities are the respective nodes of the first graph; and each second edge indicates that the two second entities linked by the second edge are part of a pattern of the predefined patterns. A processor inputs the multi-relational graphs to a multi-relational graph neural network for generating output in accordance with the specific ontology. A processor provides the output.