Delta Processing Network Nodes Efficient Graph Traversal
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Solution Overview
Problem
Large networks, such as social networks, face challenges in data privacy, identity management, and computational efficiency due to their dynamic nature, making it cumbersome to perform data analytics and traverse graphs efficiently.
Innovation Solution
The implementation of delta processing with a pre-compute solution and drip-fed delta aggregation, which updates and processes network connections in a queue to generate direct access vectors for efficient graph traversal, while ensuring secure data retrieval based on user permissions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional graph traversal methods are used on large dynamic networks, then complete graph analysis can be performed, but computational efficiency deteriorates and processing time increases
Solution Approach 1:
The patent segments the large network graph into smaller subgraphs or partitions, allowing independent processing of each segment. This enables parallel computation and reduces the computational burden on any single processing unit, thereby improving overall computational efficiency while maintaining complete graph analysis capability through aggregation of segment results
Solution Approach 2:
The patent performs preliminary actions by pre-computing and storing graph properties, adjacency matrices, or traversal paths before actual queries. This preprocessing creates indexes or cached structures that accelerate subsequent graph traversal operations, resolving the contradiction between analysis completeness and processing speed
2Measurement precision
If the network structure is updated frequently to maintain dynamic connectivity, then network accuracy is improved, but data integrity management becomes more complex
Solution Approach 1:
The patent implements feedback mechanisms that automatically detect, validate, and reconcile data changes across the dynamic network. When nodes or edges are updated, the system propagates changes through the graph structure, validates consistency constraints, and triggers re-computation only where necessary, thereby maintaining network accuracy while managing complexity through automated feedback loops
Solution Approach 2:
The patent adopts dynamic data structures and algorithms that adapt to changing network conditions. The graph representation allows efficient insertion, deletion, and modification of nodes and edges without requiring complete restructuring, enabling frequent updates while maintaining data integrity through version control and consistency protocols
3Reliability
If secure data retrieval is implemented with permission checks, then data privacy is protected, but processing speed decreases
Solution Approach 1:
The patent performs permission checks and access control validations in advance, before actual data retrieval operations. User permissions, node access rights, and data sensitivity levels are pre-evaluated and cached, allowing subsequent data access to proceed with minimal overhead while maintaining privacy protection
Solution Approach 2:
The patent introduces an intermediary layer or proxy that handles secure data retrieval operations. This intermediary manages permission validation, data filtering, and access control logic separately from the core graph processing, allowing secure operations to be performed without blocking or slowing down the main computational workflow
Data Source
AI summary
Embodiments include systems and methods for managing a network of nodes with delta processing. A network with a plurality of connected nodes can be stored, the nodes representing entities of the network. One or more deltas to the network that indicate updates to the connections among the plurality of nodes can be received. The deltas can be added to a queue. The connections between the nodes of the network can be updated using a delta from the queue. The network can be processed after updating the connections between the nodes to generate a vector for a given node. One or more paths for the given node can be identified based on the generated vector.