摘要
随着社交网络的迅猛发展，其安全问题日益凸显，针对社交网络安全分析的传统方法难以满足复杂网络环境下的需求。本研究旨在构建基于图算法的社交网络安全分析模型，以期更精准地识别潜在威胁并保障社交网络的安全性。该研究利用图算法中节点与边的关系特性，将社交网络中的用户、关系及信息交互抽象为图结构，通过深度挖掘图结构特征实现对社交网络内部异常行为的有效检测。创新性地引入了多维度权重计算机制，综合考量用户行为模式、社交关系强度以及信息传播路径等多方面因素，提高了异常检测的准确性和全面性。实验结果表明，所构建的模型在识别恶意账号、虚假信息传播源等方面具有较高的查全率和查准率，相较于传统方法性能提升显著。研究表明，基于图算法的社交网络安全分析模型能够有效应对社交网络中存在的各类安全威胁，为社交网络平台的安全管理提供了新的思路和技术手段，对于维护社交网络健康稳定发展具有重要意义。
关键词：社交网络安全；图算法；异常行为检测
Abstract
With the rapid development of social networks, security issues have become increasingly prominent, and traditional methods for analyzing social network security are struggling to meet the demands of complex network environments. This study aims to construct a social network security analysis model based on graph algorithms to more accurately identify potential threats and ensure the security of social networks. By leveraging the characteristics of nodes and edges in graph algorithms, this research abstracts users, relationships, and information interactions within social networks into graph structures, enabling effective detection of internal anomalous behaviors through deep mining of graph structure features. Innovatively, a multi-dimensional weight calculation mechanism is introduced, which comprehensively considers factors such as user behavior patterns, strength of social relationships, and information propagation paths, thereby enhancing the accuracy and comprehensiveness of anomaly detection. Experimental results demonstrate that the constructed model exhibits high recall and precision rates in identifying malicious accounts and sources of misinformation dissemination, showing significant performance improvements compared to traditional methods. The research indicates that the graph algorithm-based social network security analysis model can effectively address various security threats present in social networks, providing new insights and technical means for the security management of social network platforms, which is crucial for maintaining the healthy and stable development of social networks.
Keywords：Social Network Security; Graph Algorithm; Anomaly Behavior Detection
目 录
摘要 I
Abstract II
一、绪论 1
(一) 研究背景与意义 1
(二) 国内外研究现状 1
(三) 研究方法概述 2
二、图算法在社交网络中的应用基础 2
(一) 社交网络图模型构建 2
(二) 关键图算法介绍 3
(三) 图算法的适用性分析 4
三、基于图算法的安全威胁识别 4
(一) 异常行为模式检测 4
(二) 恶意节点识别方法 5
(三) 社区结构与安全关联 6
四、安全分析模型的设计与实现 6
(一) 模型架构设计原则 6
(二) 核心算法集成方案 7
(三) 模型性能评估体系 8
结 论 10
参考文献 11
随着社交网络的迅猛发展，其安全问题日益凸显，针对社交网络安全分析的传统方法难以满足复杂网络环境下的需求。本研究旨在构建基于图算法的社交网络安全分析模型，以期更精准地识别潜在威胁并保障社交网络的安全性。该研究利用图算法中节点与边的关系特性，将社交网络中的用户、关系及信息交互抽象为图结构，通过深度挖掘图结构特征实现对社交网络内部异常行为的有效检测。创新性地引入了多维度权重计算机制，综合考量用户行为模式、社交关系强度以及信息传播路径等多方面因素，提高了异常检测的准确性和全面性。实验结果表明，所构建的模型在识别恶意账号、虚假信息传播源等方面具有较高的查全率和查准率，相较于传统方法性能提升显著。研究表明，基于图算法的社交网络安全分析模型能够有效应对社交网络中存在的各类安全威胁，为社交网络平台的安全管理提供了新的思路和技术手段，对于维护社交网络健康稳定发展具有重要意义。
关键词：社交网络安全；图算法；异常行为检测
Abstract
With the rapid development of social networks, security issues have become increasingly prominent, and traditional methods for analyzing social network security are struggling to meet the demands of complex network environments. This study aims to construct a social network security analysis model based on graph algorithms to more accurately identify potential threats and ensure the security of social networks. By leveraging the characteristics of nodes and edges in graph algorithms, this research abstracts users, relationships, and information interactions within social networks into graph structures, enabling effective detection of internal anomalous behaviors through deep mining of graph structure features. Innovatively, a multi-dimensional weight calculation mechanism is introduced, which comprehensively considers factors such as user behavior patterns, strength of social relationships, and information propagation paths, thereby enhancing the accuracy and comprehensiveness of anomaly detection. Experimental results demonstrate that the constructed model exhibits high recall and precision rates in identifying malicious accounts and sources of misinformation dissemination, showing significant performance improvements compared to traditional methods. The research indicates that the graph algorithm-based social network security analysis model can effectively address various security threats present in social networks, providing new insights and technical means for the security management of social network platforms, which is crucial for maintaining the healthy and stable development of social networks.
Keywords：Social Network Security; Graph Algorithm; Anomaly Behavior Detection
目 录
摘要 I
Abstract II
一、绪论 1
(一) 研究背景与意义 1
(二) 国内外研究现状 1
(三) 研究方法概述 2
二、图算法在社交网络中的应用基础 2
(一) 社交网络图模型构建 2
(二) 关键图算法介绍 3
(三) 图算法的适用性分析 4
三、基于图算法的安全威胁识别 4
(一) 异常行为模式检测 4
(二) 恶意节点识别方法 5
(三) 社区结构与安全关联 6
四、安全分析模型的设计与实现 6
(一) 模型架构设计原则 6
(二) 核心算法集成方案 7
(三) 模型性能评估体系 8
结 论 10
参考文献 11
