Connected Graph Attribute Propagation for Real-Time Content Alignment
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Solution Overview
Problem
Existing content distribution systems fail to efficiently propagate attribute changes, such as price adjustments, across related content items, leading to misalignment and potential feedback loops, without leveraging changes in one content item to affect others.
Innovation Solution
A method and system for attribute propagation using a connected graph model that efficiently disseminates changes in attributes, like prices, across content items by designating transmitter nodes and applying a cascade model with mechanisms to manage feedback loops and ensure real-time alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing content distribution systems are used to manage related content items, then content items can be stored and referenced, but attribute changes cannot be propagated across related content items leading to misalignment
Solution Approach 1:
The system segments content items into a graph structure where each content item is a node and relationships are edges. This segmentation allows independent tracking and propagation of attribute changes through specific pathways in the graph, ensuring that attribute updates are systematically distributed to related content items without losing synchronization.
Solution Approach 2:
The system implements feedback mechanisms where attribute changes in one content item trigger automatic propagation to related content items through the graph structure. This feedback loop ensures that all related content items remain aligned with the latest attribute changes, preventing misalignment and information loss across the content network.
2Productivity
If attribute changes are propagated across all related content items, then real-time alignment is achieved, but feedback loops and cyclic updates may occur increasing system complexity
Solution Approach 1:
The system performs preliminary actions by establishing a graph structure with defined relationships and propagation rules before attribute changes occur. This pre-configured structure enables rapid attribute propagation while preventing feedback loops through predetermined update pathways and directionality in the graph edges, reducing the need for complex runtime feedback management.
Solution Approach 2:
The system employs dynamic update mechanisms where attribute propagation adapts to the current state of the graph. Updates are propagated dynamically through the graph structure with automatic detection of already-updated nodes, preventing cyclic updates while maintaining real-time alignment. The propagation process adjusts its behavior based on the current attribution state, avoiding infinite loops.
3Reliability
If a connected graph model is used to propagate attributes, then real-time synchronization is achieved, but computational overhead and processing time increase
Solution Approach 1:
The system applies local quality by propagating attribute changes only to locally connected nodes in the graph rather than globally updating all content items. This localized propagation approach maintains reliable synchronization among related content items while significantly reducing computational overhead and processing time compared to global updates.
Solution Approach 2:
The system performs partial updates by propagating attribute changes only to the extent necessary—specifically, only to related content items that have not yet been updated. This partial action approach achieves sufficient synchronization reliability without the excessive computational cost of redundant or complete graph traversals, optimizing the balance between synchronization quality and processing time.
Data Source
AI summary
A method, apparatus, and computer-readable medium for attribute propagation, including storing a connected graph comprising a plurality of node data structures corresponding to a plurality of content items, detecting a change in the attribute of a node data structure in the plurality of node data structures, the change in the attribute being represented by a delta value of the node data structure, designating the node data structure as a transmitter node, and propagating the change in the attribute of the node data structure to connected node data structures in the connected graph.