Cluster Node Property Retrieval in Social Graphs
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Solution Overview
Problem
Social networking systems face challenges in efficiently rendering content objects by accessing and providing missing property information for concept nodes, especially when nodes are part of clusters sharing similar properties, leading to incomplete or missing data in user interactions and content display.
Innovation Solution
The system determines if a requested concept node is part of a cluster and, if so, accesses the cluster to retrieve missing property values from related nodes, ensuring complete information is provided for rendering, such as book titles and cover images, by identifying a 'best' node within the cluster based on criteria like popularity and credibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the system accesses and provides missing property information for concept nodes by querying clusters of related nodes, then data completeness and accuracy are improved, but system complexity and query processing time increase
Solution Approach 1:
The system pre-identifies and caches cluster relationships between concept nodes during system initialization or idle periods. When a node is queried, the pre-computed cluster associations are immediately retrieved without performing complex graph traversals at query time, thus reducing processing complexity while maintaining data completeness.
Solution Approach 2:
The patent introduces cluster nodes as intermediary structures that aggregate properties from multiple related concept nodes. Instead of directly querying individual nodes or performing complex multi-node queries, the system uses cluster nodes as mediators that pre-consolidate information, simplifying the query process while ensuring complete data retrieval.
2Measurement precision
If the system retrieves property information from multiple cluster nodes to ensure data completeness, then data accuracy is improved, but query processing time and computational resources increase
Solution Approach 1:
The system merges multiple concept nodes into unified cluster nodes that contain aggregated property information from all member nodes. When querying for accurate data, the system queries the single consolidated cluster node rather than separately querying multiple individual nodes, thereby maintaining data accuracy while significantly reducing query processing time and computational overhead.
Solution Approach 2:
The system performs preliminary consolidation of property information from multiple nodes into cluster nodes during system initialization or idle periods. This pre-aggregation ensures that when queries are executed, the accurate consolidated data is already available in the cluster node, eliminating the need for time-consuming runtime data gathering from multiple sources.
3Loss of substance
If the system implements cluster-based node relationships to share property information, then data redundancy is reduced, but system complexity and difficulty of implementation increase
Solution Approach 1:
The patent segments the system into distinct hierarchical levels: individual concept nodes, cluster nodes (which group related concepts), and property information. This segmentation allows the system to eliminate redundancy at the cluster level while maintaining simple, well-defined interfaces and relationships between levels, making the system easier to implement and manage despite the added organizational structure.
Solution Approach 2:
The system adds a hierarchical dimension by introducing cluster nodes as an intermediate layer between individual concept nodes. Instead of creating complex many-to-many relationships between nodes at the same level, the patent organizes nodes into hierarchical clusters, reducing redundancy while maintaining manageable system complexity through clear hierarchical boundaries and relationships.
Data Source
AI summary
In one embodiment, one or more server computing devices receive, from a client computing device, a request for first information associated with a first node of a graph. The one or more server computing devices determine whether the first node is associated with a cluster of nodes. A cluster of nodes includes one or more concept nodes of the graph that are related to each other. When the first node is associated with a cluster of nodes, the one or more server computing devices access the cluster of nodes that the first node is associated with, obtain second information from one or more of the nodes in the cluster of nodes that the first node is associated with, and provide the second information for rendering by the client computing device.