User Relationship Analysis Model for Social Network Prediction
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Solution Overview
Problem
Existing user relationship analysis technologies in social networks suffer from low accuracy in relationship prediction, as they primarily focus on network topology information without considering other influential factors.
Innovation Solution
A method that involves acquiring and processing original data to extract target information, defining user relationships, performing node feature extraction based on influencing factors, and constructing a user relationship analysis model to improve prediction accuracy by incorporating personal interests, friend relationships, and community drives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only network topology information is considered for user relationship prediction, then the analysis process is simple, but the prediction accuracy is low
Solution Approach 1:
The patent segments the user relationship analysis into multiple independent modules: user information extraction module, network topology analysis module, and relationship prediction module. Each module processes specific types of information (demographic data, network structure, interaction patterns) separately before integrating them in the prediction module, thereby improving accuracy while maintaining manageable complexity through modular architecture
Solution Approach 2:
The patent transitions from two-dimensional network topology analysis to multi-dimensional analysis by incorporating additional dimensions such as user demographic characteristics, interaction frequency, temporal patterns, and content-based features. This dimensional expansion enables the system to capture richer relationship characteristics and significantly improves prediction accuracy without overwhelming complexity
2Measurement precision
If multiple types of information are integrated for user relationship analysis, then the prediction accuracy improves, but the data processing complexity increases
Solution Approach 1:
The patent performs preliminary data processing actions in dedicated extraction modules before the main prediction process. User information is extracted and pre-processed, network topology is pre-analyzed, and interaction patterns are pre-computed. This preliminary action consolidates complex data processing into manageable stages, reducing the complexity burden during the final prediction phase while maintaining comprehensive data integration
Solution Approach 2:
The patent introduces an intermediary feature extraction layer that transforms raw multi-type data into standardized feature representations. This intermediary layer acts as a mediator between diverse data sources (user profiles, network graphs, interaction logs) and the prediction model, simplifying the integration process and reducing processing complexity by standardizing data formats and representations
Data Source
AI summary
The present disclosure relates to user relationship analysis technology, and discloses a method, electronic apparatus, and storage medium for analyzing user relationships in a social network. The method includes: acquiring original data of training users, and performing training user information division on the original data to obtain target information; defining relationships between the training users according to the target information to obtain a user relationship network; performing node feature extraction on the user relationship network according to preset influencing factors to obtain feature data corresponding to the influencing factors; and constructing a user relationship analysis model based on the feature data, and performing relationship prediction on user data of a preset user to be tested using the user relationship analysis model to obtain the user relationship of the user to be tested. The present disclosure can improve the accuracy of user relationship prediction in the user relationship analysis technology.