Incremental Augmented Graph Model for Dynamic Transaction Networks
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Solution Overview
Problem
Existing methods for analyzing transactions between user accounts in computer-implemented services struggle with efficiently processing large volumes of data and identifying subsets of user accounts with shared characteristics, particularly in dynamic networks where new transactions and attributes are continuously added, leading to inefficiencies in resource utilization and accuracy.
Innovation Solution
The incremental Augmented Graph with Modularity Maximization and Refinement (inc-AGGMMR) framework allows for the incremental addition of transactions and attribute values to an augmented graph model without rebuilding the entire model, enabling the identification of user account subsets by adjusting groupings as the network evolves, using modularity maximization and refinement techniques to optimize community detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the entire augmented graph model is rebuilt to incorporate new transactions and attribute values, then the accuracy of user account subset identification is maintained, but the computational resources and time required increase significantly
Solution Approach 1:
The patent segments the graph update process into incremental operations. Instead of rebuilding the entire augmented graph model, only the affected subgraph containing new nodes and their connected components is updated. This segmentation allows the system to maintain accuracy by updating relevant portions while avoiding the computational overhead of full model reconstruction.
Solution Approach 2:
The patent applies preliminary action by pre-computing and storing graph structures, community assignments, and attribute information in an optimized format. When new transactions arrive, the system leverages these pre-computed structures to rapidly integrate new data without starting from scratch, thus maintaining accuracy while reducing update time.
2Measurement precision
If the entire augmented graph model is rebuilt to incorporate new transactions and attribute values, then the accuracy of user account subset identification is maintained, but the computational resources required increase significantly
Solution Approach 1:
The patent segments the computational workload by updating only the affected subgraph rather than the entire augmented graph model. This segmentation reduces the computational resources required while maintaining the accuracy of user account subset identification through localized updates that preserve the integrity of community structures.
Solution Approach 2:
The patent uses copying by maintaining copies of graph structures and community assignments in optimized formats. When updating with new transactions, the system copies and modifies only the necessary portions rather than processing the entire model, thereby reducing computational resource consumption while preserving accuracy.
3Quantity of substance
If traditional graph analysis methods are used to process large volumes of transaction data, then comprehensive analysis can be performed, but the processing efficiency decreases
Solution Approach 1:
The patent segments the large volume of transaction data into manageable subgraphs based on community structures and node connections. This segmentation enables the system to process comprehensive transaction data while maintaining high processing efficiency by focusing computational efforts on relevant subsets rather than analyzing the entire dataset uniformly.
Solution Approach 2:
The patent applies dynamics by implementing an adaptive, incremental update mechanism that dynamically adjusts the analysis scope based on new transactions. The system processes large volumes of data efficiently by dynamically identifying and updating only the affected portions of the augmented graph, rather than statically processing the entire dataset each time.
4Productivity
If the augmented graph model is updated incrementally to add new transactions, then processing efficiency improves, but the complexity of managing partial updates increases
Solution Approach 1:
The patent implements feedback mechanisms that automatically track which nodes and edges are affected by new transactions. This feedback system manages the complexity of partial updates by providing structured information about what needs to be updated, reducing the manual complexity of incremental graph maintenance while preserving processing efficiency.
Solution Approach 2:
The patent uses an intermediary mechanism in the form of an augmented graph structure that mediates between raw transaction data and the final community detection results. This intermediary structure simplifies the management of partial updates by providing a standardized framework for integrating new data, thereby reducing complexity while maintaining high processing efficiency.
Data Source
AI summary
A computer system accesses an augmented graph model of (a) a set of transactions previously performed between respective pairs of initiator user accounts of a service and recipient user accounts of the service and (b) attribute values for a subset of the recipient user accounts. The computer system receives additional information indicative of an additional transaction involving an additional recipient user account that is not represented in the augmented graph model with a node. The computer system modifies the augmented graph model using the additional information and groups the user accounts represented in the modified augmented graph model into a plurality of groups.