Relationship Graph Construction via Chat Group Nodes
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Solution Overview
Problem
Existing methods for building and updating relationship graphs among users are inefficient due to the use of large amounts of data, leading to high computational complexity and noise, making them less accurate and resource-intensive.
Innovation Solution
A system and method that utilizes online chat communication groups to build and update relationship graphs by focusing on concise data, such as common chat groups joined by users, where nodes represent users and edges indicate shared groups, with weights representing the number of common groups, allowing for efficient updates by only processing changed nodes and edges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If large amounts of data are used to build relationship graphs, then the graph can capture more user relationships, but computational complexity increases and processing becomes less efficient
Solution Approach 1:
The patent extracts only the essential and relevant data elements needed for relationship graph construction, specifically focusing on user profile information, interaction data, and connection information. By filtering out unnecessary data, the system achieves accurate relationship modeling without the computational burden of processing large volumes of irrelevant information.
Solution Approach 2:
The patent applies different data processing strategies to different parts of the relationship graph construction process. User profile data, interaction data, and connection data are processed with appropriate levels of detail and complexity matched to their specific purposes, optimizing overall system efficiency while maintaining accuracy where needed.
2Measurement precision
If comprehensive user data is processed to build relationship graphs, then relationship accuracy improves, but processing time increases
Solution Approach 1:
The patent performs preliminary processing and organization of user data before constructing the relationship graph. User profiles, interactions, and connections are pre-structured and indexed, enabling faster query processing and relationship detection without sacrificing accuracy during the actual graph construction phase.
Solution Approach 2:
The patent processes data selectively rather than comprehensively, focusing on the specific subsets of user information most relevant to relationship detection. By applying partial action to the most critical data elements, the system achieves high accuracy while minimizing processing time.
3Measurement precision
If extensive user data is analyzed to build relationship graphs, then relationship identification accuracy improves, but resource consumption increases
Solution Approach 1:
The patent extracts and processes only the essential data elements required for accurate relationship identification, such as user profiles, interaction histories, and connection information. By eliminating unnecessary data processing, the system maintains high identification accuracy while significantly reducing computational resource consumption.
Solution Approach 2:
The patent optimizes data processing parameters and thresholds to achieve the best balance between relationship identification accuracy and resource consumption. By carefully tuning parameters such as interaction weight thresholds and connection criteria, the system maximizes accuracy while minimizing the computational resources required.
Data Source
AI summary
Systems and methods for building and updating a relationship graph based on online chat communication groups are provided. In an example, a computing device receives chat communication group user information which includes user-group records each indicating a user and a chat communication group joined by the user. The computing device builds a relationship graph for chat communication groups based on the chat communication group user information with nodes representing respective users, an edge connecting two nodes indicating that two users are in at least one common chat group, and the weight of the edge representing the number of common chat communication groups that the two users belong to. The device further generates graph updates based on chat user change records describing user changes occurred during a time period and transmits the graph updates to a remote computing device for updating the relationship graph and identifying relationship among the users.