Social Graph Query Filtering via Privacy-Aware Node Detection
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Solution Overview
Problem
Current social networking systems face challenges in efficiently searching and filtering social graph elements within complex network structures, particularly in resolving privacy settings and generating personalized structured search queries that effectively navigate user and concept profiles.
Innovation Solution
The implementation of a method that utilizes a social graph database to generate structured queries based on social-graph information, including node and edge types, and integrates typeahead and bootstrapping processes to automatically create and connect user and concept nodes, while filtering search results based on privacy settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated node and edge creation is implemented to improve search query personalization, then search effectiveness is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by automatically creating user nodes and concept nodes, and establishing edges between them, before search queries are executed. This pre-processing of social graph elements enables personalized search without requiring manual setup, resolving the contradiction by automating complexity-handling tasks in advance.
Solution Approach 2:
The system implements self-service through automated detection and creation of social graph elements based on search queries. The system serves itself by autonomously generating nodes and edges without manual intervention, improving search personalization while managing complexity through automation rather than manual configuration.
2Object-affected harmful factors
If privacy settings are enforced to protect user data, then user privacy is protected, but search result completeness is reduced
Solution Approach 1:
The system applies local quality by implementing privacy settings at the individual user node and edge level rather than globally. Each social graph element can have its own privacy attributes, allowing the system to protect specific user data while still returning comprehensive search results that include all non-private information, thus maintaining both privacy protection and result completeness.
Data Source
AI summary
In particular embodiments, a method includes receiving an unstructured text query, identifying nodes and edges from a social graph that correspond to n-grams in the text query, generating structured queries that include references to the identified nodes and edges, receiving a selection of a structured query, identifying target nodes that correspond to the structured query, and then generating search results that include target nodes with privacy settings where the nodes and edges along the path connecting the target node and the querying user are all visible to the user.