Social Network Graph Layered Group Search Expansion
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Solution Overview
Problem
Current methods for searching groups in social networks are inefficient due to the need to scan all possible combinations of nodes, resulting in high computational costs and low search efficiency.
Innovation Solution
A method and device that perform search expansion using a social network graph, starting from a query user ID, to determine group members with the maximum number of adjacent nodes in each layer, forming a group that meets specified scale and coritivity criteria, and verifying the group's coritivity before returning it as a query result.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all possible combinations of nodes are determined and scanned according to group number, then complete group search is achieved, but computational burden increases and search efficiency decreases
Solution Approach 1:
The patent segments the group search process into layered expansion stages, where nodes are searched layer by layer based on their distance from the target node. This segmentation avoids the need to evaluate all possible node combinations simultaneously, reducing computational complexity while maintaining search completeness through systematic layer-by-layer exploration.
Solution Approach 2:
The patent performs preliminary actions by pre-calculating and storing the distance between nodes before the actual group search. This preliminary distance information is used to guide the layered expansion process, allowing the algorithm to efficiently determine which nodes to explore next without having to recalculate distances during the search, thus improving search efficiency.
2Productivity
If layered search expansion is performed to improve search efficiency, then computational burden is reduced, but search completeness may be compromised
Solution Approach 1:
The patent employs a dynamic layered expansion approach where the search layers are adaptively adjusted based on the group size requirement and coritivity constraints. The expansion continues dynamically layer by layer until the required number of group members is found, ensuring both efficiency through layered structure and completeness through adaptive continuation of the search process.
Solution Approach 2:
The patent incorporates feedback mechanisms where each layer's search results are evaluated against the group coritivity requirement. If the coritivity condition is not met, the search expands to the next layer, using feedback from previous layers to guide subsequent expansion. This feedback-driven approach ensures that the search remains complete while maintaining efficiency through targeted expansion.
Data Source
AI summary
A method of searching a group based on a social network includes: receiving a group search request transmitted by a query terminal, wherein the group search request contains a specified query user identification (ID), a group scale and a group coritivity; retrieving a pre-generated social network graph; using the user node corresponding to the query user ID as a starting node to perform search expansion according to the social network graph, and determining a user node having the maximum number of adjacent nodes in each search expansion layer as a group member until the number of determined group members equals to the group scale in the social network graph; a first level of the expansion layer includes the user node, which is an adjacent node of the query user, and the user node included in a next level of expansion layer is an adjacent node of the group member determined by the upper level of the expansion layer; and determining that a group coritivity of the group to be searched is not less than the specified group coritivity, and returning the group to be searched as a query result to the query terminal for displaying.