Network Effect Classification in Distributed File Systems
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Solution Overview
Problem
Existing network systems face challenges in understanding and managing relationships between nodes, leading to increased processing power requirements and reduced effectiveness, as individual node analysis fails to reveal the network effects of actions taken on one node across the network.
Innovation Solution
A system that identifies and classifies links between nodes, calculates network effects, and modifies action parameters based on these effects, allowing for actions such as customer acquisition, marketing, or fraud management by displaying nodes and relationships in entity graphs and using confidence scores to predict the impact of actions on secondary nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If individual node analysis is performed, then processing power requirements are reduced, but network effectiveness is reduced due to inability to understand network effects
Solution Approach 1:
The system segments the network into meaningful clusters by classifying links between nodes into different types (self-match, co-relation match, multi-entity match). This allows the system to process and analyze network relationships in manageable segments rather than treating the entire network as one complex entity, thereby reducing processing power requirements while maintaining network effectiveness through targeted analysis of relevant node relationships.
2Productivity
If link classification and network effect calculation are performed, then network effectiveness is improved, but computational time and resource usage increase
Solution Approach 1:
The system applies local quality by calculating network effects only for specific link classifications (self-match, co-relation match, multi-entity match) rather than uniformly analyzing all node relationships. This targeted approach allows the system to improve network effectiveness by focusing computational resources on the most relevant link types while reducing overall computational time and resource usage.
Solution Approach 2:
The system changes parameters by using confidence scores to weigh different link types differently in network effect calculations. By adjusting the importance parameters of different link classifications, the system can optimize the balance between network effectiveness and computational efficiency, reducing computational time while maintaining accurate network effect analysis.
3Loss of information
If comprehensive network analysis is performed, then understanding of relationships is improved, but data storage and memory needs increase
Solution Approach 1:
The system extracts and stores only the essential link classification information (self-match, co-relation match, multi-entity match) and confidence scores rather than storing complete network relationship data. This extraction approach allows the system to maintain comprehensive understanding of relationships through the classified link structures while significantly reducing data storage and memory requirements compared to storing all raw network data.
Data Source
AI summary
A distributed file system may store a plurality of entity attributes. A node linking system may classify links between the nodes. The node linking system may calculate a network effect of an action with a link. The node linking system may modify parameters of the action based on the network effect.