Graph Search Visualization for Fraudulent Transaction Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional techniques for searching and visualizing large datasets represented as graphs are inefficient, particularly in identifying similarities between graphs or patterns within a graph, which is crucial for fraud detection in transaction networks.
Innovation Solution
The implementation of graph embedding techniques, such as NetBoost and Diffpool, combined with clustering, to efficiently generate vectors for graphs, identify patterns, and provide a system for querying and exploring graph similarities, thereby enhancing the scalability and latency of graph searching and exploration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional searching techniques are used to explore graphs representing large datasets, then the system is simpler to implement, but the search speed and efficiency of identifying similarities and patterns deteriorates
Solution Approach 1:
The patent replaces conventional mechanical graph traversal algorithms with graph embedding techniques that transform graphs into vector representations. This substitution enables faster similarity search through vector space operations rather than traditional graph walking, directly addressing the speed-complexity contradiction by using more efficient computational representations.
Solution Approach 2:
The patent changes the representation parameters of graphs from traditional adjacency matrices or edge lists to embedded vector representations. This parameter transformation allows for efficient similarity computation using distance metrics in vector space, significantly improving search speed while maintaining the ability to capture complex graph patterns.
2Measurement precision
If conventional graph exploration techniques are used, then the implementation is simpler, but the ability to efficiently identify similarities between graphs and patterns within graphs deteriorates
Solution Approach 1:
The patent substitutes traditional mechanical graph traversal and manual pattern recognition with automated graph embedding and vector-based similarity computation. This replacement enables both high pattern identification accuracy and efficient detection by transforming complex graph comparison tasks into straightforward vector distance calculations.
Solution Approach 2:
The patent creates vector representations (embeddings) that capture the essential structure and patterns of graphs. These vector copies preserve the important graph characteristics while enabling efficient similarity search, allowing the system to identify patterns accurately without the computational burden of comparing full graph structures.
3Productivity
If graph embedding techniques are applied to handle large datasets, then the scalability and latency of graph searching improves, but the computational resources and processing complexity increases
Solution Approach 1:
The patent performs graph embedding computations in advance during data ingestion or batch processing, rather than performing them during real-time queries. This preliminary action stores pre-computed vector representations that can be quickly searched and compared, significantly improving query productivity while reducing the computational energy consumption during actual search operations.
Solution Approach 2:
The patent segments the graph processing task into two distinct phases: an offline embedding computation phase that processes entire graphs and creates vector representations, and an online query phase that performs fast similarity search using the pre-computed vectors. This segmentation allows the system to achieve high productivity during queries while minimizing real-time computational energy consumption.
Data Source
AI summary
In an embodiment, a process for graph search and visualization includes receiving a query graph. The process includes calculating one or more vectors for the query graph, wherein the one or more vectors each identifies a corresponding portion of the query graph. The process includes identifying one or more graphs similar to the query graph including by comparing the calculated one or more vectors for the query graph with one or more previously-calculated vectors for a different set of graphs. The process includes outputting the identified one or more similar graphs.