A method for matching continuous subgraphs in a social network based on dynamic pruning.

By employing a dynamic pruning algorithm and a query graph decomposition and sorting mechanism, isomorphic subtrees in the dynamic graph of social networks are identified and the matching order is optimized. This solves the problems of redundant computation and insufficient matching order in the enumeration stage of existing algorithms, achieving efficient detection of abnormal behavior in social networks and improving matching speed and real-time performance.

CN121234065BActive Publication Date: 2026-06-30NORTHEASTERN UNIV CHINA
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Patent Information

Application Number
CN202511465824.0
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-10-14
Publication Date
2026-06-30
Estimated Expiration
2045-10-14

AI Technical Summary

Technical Problem

Existing dynamic graph matching algorithms for social networks employ brute-force search during the enumeration phase, resulting in repeated traversal of numerous isomorphic subtrees. Furthermore, the matching order fails to adequately consider the pruning potential of leaf query points, leading to a sharp performance degradation in complex query scenarios and an inability to meet the real-time requirements for detecting abnormal behavior in social networks.

Method used

A dynamic pruning algorithm is adopted to establish local equivalence theory by calculating the backward neighbors of query points and data candidate points, identify isomorphic subtree structures and perform pruning, and combine the query graph decomposition and sorting mechanism to prioritize the processing of vertices in highly redundant candidate sets, construct the matching order of priority layer, pruning layer and delay layer to reduce the search space.

Benefits of technology

It significantly improves the real-time performance and accuracy of abnormal behavior detection on social networks, increases matching speed by 16.9 times, reduces redundant computation by 90%, maintains millisecond-level response capability in high-concurrency dynamic update environments, and supports real-time fraud warnings and malicious behavior tracking on social platforms.

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Abstract

This invention provides a continuous subgraph matching method for social networks based on dynamic pruning, belonging to the field of social network behavior detection. The invention proposes a dynamic pruning algorithm that establishes local equivalence theory by calculating the backward neighbors of query points and candidate points, defines subtree-level equivalence criteria in dynamic graph environments, and is applicable to highly isomorphic behavior patterns such as zombie account groups and terminal nodes of propagation chains. The dynamic pruning algorithm skips isomorphic subtree structures to achieve the pruning effect. A query graph decomposition and sorting mechanism is proposed to remove the binding constraint of the matching order on the update edges and candidate sets, actively identifying and prioritizing highly redundant candidate set vertices, further reducing the search space. Ultimately, while ensuring result completeness, it achieves a 16.9-fold performance improvement over RapidFlow and a 100% completion rate for complex queries, providing sub-second decision support for abnormal behavior detection in social networks and promoting the application of dynamic graph analysis in resource-constrained environments such as edge computing.
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