Graph Substructure Classification via Textual Hash Maps
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Solution Overview
Problem
Identifying specific substructures in large graph datasets is computationally expensive and time-consuming due to the presence of repetitive substructures, which hinders efficient data classification and analysis.
Innovation Solution
A system that uses a hash map to efficiently identify and consolidate repetitive substructures in graph data by generating textual descriptions for nodes and edges, and grouping substructures based on shared descriptions and common edges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional graph traversal methods are used to identify substructures, then measurement precision is maintained, but processing time increases significantly and computational efficiency deteriorates
Solution Approach 1:
The patent generates textual descriptions for all nodes and edges in advance, and pre-processes the graph data to create a structured representation. This preliminary action allows the system to quickly identify substructures without performing exhaustive traversals during query time, thus reducing processing time while maintaining identification accuracy
Solution Approach 2:
The patent introduces textual descriptions as an intermediary representation between the graph structure and substructure identification. By converting graph elements into text and using string matching operations, the system achieves efficient substructure detection without direct graph traversal, resolving the contradiction between precision and speed
2Measurement precision
If exhaustive graph traversal is performed to ensure accurate substructure identification, then measurement precision is maintained, but computational power consumption increases
Solution Approach 1:
The patent replaces the mechanical graph traversal process with text-based operations. Instead of systematically visiting nodes and edges through complex traversal algorithms, the system uses string matching and comparison operations on pre-generated textual descriptions, significantly reducing computational power consumption while maintaining identification accuracy
Solution Approach 2:
The patent creates textual copies of graph elements (nodes and edges) and operates on these copies rather than the original graph structure. This copying approach allows efficient substructure identification through text processing, reducing the computational burden on the original graph data while preserving measurement precision
3Measurement precision
If detailed analysis of each substructure is performed, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the graph into individual textual representations of nodes and edges. By dividing the complex graph analysis into separate text processing components, the system achieves detailed substructure analysis through simple string operations, improving precision without increasing overall system complexity
Data Source
AI summary
There are provided systems and methods for classifying a substructure in graph data using textual descriptions derived from the graph data and hash maps generated from the textual descriptions. One or more descriptions that describe a plurality of transactions are generated based on a sequence of nodes in a set of nodes identified in a corresponding transaction of the plurality of transactions. The set of nodes represents features of the corresponding transaction. A plurality of instances is identified from the descriptions, and each instance may include a substructure including two or more nodes connected via at least one edge. For each instance, a key and a value are generated based on a hash for the instance to generate a hash map. A specific transaction is classified as an abnormal transaction based on a particular key that is excluded from the hash map.