Social Graph Node Generation via Concept Database

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Solution Overview

Problem

Conventional social networking platforms lack efficient methods for automatically generating nodes and edges in social graphs and providing personalized recommendations to users based on real-time and historical user interactions and interests.

Innovation Solution

The implementation of a social network environment that uses typeahead and bootstrapping features to automatically generate nodes and edges based on user input, leveraging a concept database populated from internal and external sources to provide dynamic recommendations by analyzing user interactions and interests.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If conventional social networking platforms manually create nodes and edges in social graphs, then the social graph structure can be maintained, but the process is time-consuming and lacks real-time responsiveness to user interactions

Engineering Contradiction:

Improvenode and edge generation efficiencyVSAvoidtime for manual graph maintenance

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system automatically generates nodes and edges in the social graph by analyzing user interactions, profile information, and content relationships without requiring manual intervention. The platform self-updates the social graph structure in real-time as users perform actions like posting content, liking, commenting, or forming connections, thereby eliminating the need for manual graph maintenance while keeping the structure current and responsive

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system pre-processes user interactions and content data to identify potential relationships and connections before they are formally added to the social graph. By analyzing user behavior patterns, profile similarities, and content relationships in advance, the system prepares node and edge candidates that can be quickly integrated into the social graph structure when conditions are met, reducing the time required for graph updates

Inventive Principle:

Principle #10Preliminary action

2Quantity of substance

If the social graph includes all possible user connections and interactions, then the network coverage is comprehensive, but the complexity of analyzing and processing the graph increases significantly

Engineering Contradiction:

Improvenumber of nodes and edges in social graphVSAvoidgraph analysis and processing complexity

Core Design Contradiction:

Quantity of substanceVSDevice complexity

Solution Approach 1:

The system applies different processing and analysis methods to different regions or portions of the social graph based on their characteristics. Highly connected user clusters with dense interactions receive more sophisticated analysis, while less active users are processed with simpler methods. This localized approach allows comprehensive coverage of all users while optimizing computational resources by adapting the analysis complexity to the actual engagement levels of different graph regions

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The social graph is divided into multiple segments or communities based on user interactions, interests, and connection patterns. Each segment can be analyzed and processed independently, reducing the overall computational complexity. The system identifies community structures within the large graph and performs localized recommendations and analyses on each segment rather than processing the entire graph uniformly, thereby managing complexity while maintaining comprehensive coverage

Inventive Principle:

Principle #1Segmentation

3Measurement precision

If recommendations are generated based on detailed analysis of user interactions and interests, then the personalization accuracy improves, but the computational resources and time required increase

Engineering Contradiction:

Improverecommendation personalization accuracyVSAvoidcomputational energy for recommendation generation

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system generates recommendations using a tiered approach where basic recommendations are provided to all users based on general patterns, while more detailed and computationally intensive personalized recommendations are generated only for users who actively engage with the platform or show specific interest signals. This partial application of deep analysis to only those who need it maintains high personalization accuracy for engaged users while reducing overall computational energy consumption across the entire user base

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS10331748B2Dynamically generating recommendations based on social graph information

Publication Date: 2019.06.25 META PLATFORMS INC

AI summary

In one embodiment, a method includes receiving, at a query field associated with a social network environment, a request from a first user of the social network environment, identifying a first set of objects associated with the online social network matching at least in part the character string of the request, determining a second set of objects based on the first set of objects, wherein each object in the second set of objects is conceptually relevant to at least one of the objects of the first set of objects, and sending, to the query field for display to the first user, the suggested queries responsive to the request, wherein at least one of the suggested queries comprises a references to one of the conceptually relevant objects in the second set of objects.