摘 要
社交网络的迅猛发展催生了海量关系型数据，传统关系型数据库在处理复杂关联查询时效率低下且难以应对动态变化的社交结构。基于图数据库的社交网络数据挖掘与分析旨在利用图数据库高效存储和查询复杂关系的优势，以实现对社交网络中用户行为、社区结构及信息传播等多维度特征的深入剖析。本研究聚焦于构建适用于社交网络特性的图数据库模型，通过引入节点属性权重调整机制优化数据存储结构，并设计基于随机游走与深度优先搜索相结合的混合算法进行社区发现。同时，提出一种融合时间衰减因子的信息传播路径预测模型，有效捕捉社交网络中信息流动规律。实验结果表明，所构建的图数据库模型在查询响应时间和存储空间利用率方面较传统方法提升显著，混合算法能够准确识别出具有实际意义的社区结构，信息传播路径预测模型的精度达到85%以上。该研究不仅为社交网络数据挖掘提供了新的技术手段，而且对于理解社交网络内在机制、防范不良信息扩散等方面具有重要理论价值和应用前景。
关键词：社交网络数据挖掘；图数据库模型；社区发现算法
Abstract
The rapid development of social networks has generated a massive amount of relational data, posing significant challenges for traditional relational databases in handling complex association queries and adapting to the dynamic changes in social structures. This study focuses on social network data mining and analysis using graph databases, leveraging their efficiency in storing and querying complex relationships to achieve in-depth analysis of multi-dimensional features such as user behavior, community structure, and information diffusion within social networks. Specifically, this research aims to construct a graph database model tailored for social network characteristics by introducing a node attribute weight adjustment mechanism to optimize the data storage structure. A hybrid algorithm combining random walk and depth-first search is designed for community detection. Additionally, a time-decay factor integrated information propagation path prediction model is proposed to effectively capture the patterns of information flow in social networks. Experimental results demonstrate that the constructed graph database model significantly improves query response time and storage space utilization compared to traditional methods. The hybrid algorithm accurately identifies meaningful community structures, while the information propagation path prediction model achieves a precision rate exceeding 85%. This research not only provides new technical approaches for social network data mining but also holds important theoretical value and application prospects in understanding the intrinsic mechanisms of social networks and preventing the spread of malicious information.
Keywords：Social Network Data Mining；Graph Database Model；Community Discovery Algorithm
目 录
引 言 1
第一章 图数据库与社交网络概述 2
1.1 图数据库基本原理 2
1.2 社交网络数据特点 2
1.3 图数据库在社交网络中的优势 3
第二章 社交网络图数据建模方法 5
2.1 数据模型选择依据 5
2.2 用户关系建模策略 5
2.3 信息传播路径建模 6
第三章 基于图数据库的挖掘算法 8
3.1 关系模式发现算法 8
3.2 社区结构识别方法 8
3.3 影响力传播分析技术 9
第四章 社交网络数据分析应用 11
4.1 用户行为模式分析 11
4.2 舆情监测与预警 11
4.3 精准营销推荐系统 12
结 论 13
参考文献 14
致 谢 15
社交网络的迅猛发展催生了海量关系型数据，传统关系型数据库在处理复杂关联查询时效率低下且难以应对动态变化的社交结构。基于图数据库的社交网络数据挖掘与分析旨在利用图数据库高效存储和查询复杂关系的优势，以实现对社交网络中用户行为、社区结构及信息传播等多维度特征的深入剖析。本研究聚焦于构建适用于社交网络特性的图数据库模型，通过引入节点属性权重调整机制优化数据存储结构，并设计基于随机游走与深度优先搜索相结合的混合算法进行社区发现。同时，提出一种融合时间衰减因子的信息传播路径预测模型，有效捕捉社交网络中信息流动规律。实验结果表明，所构建的图数据库模型在查询响应时间和存储空间利用率方面较传统方法提升显著，混合算法能够准确识别出具有实际意义的社区结构，信息传播路径预测模型的精度达到85%以上。该研究不仅为社交网络数据挖掘提供了新的技术手段，而且对于理解社交网络内在机制、防范不良信息扩散等方面具有重要理论价值和应用前景。
关键词：社交网络数据挖掘；图数据库模型；社区发现算法
Abstract
The rapid development of social networks has generated a massive amount of relational data, posing significant challenges for traditional relational databases in handling complex association queries and adapting to the dynamic changes in social structures. This study focuses on social network data mining and analysis using graph databases, leveraging their efficiency in storing and querying complex relationships to achieve in-depth analysis of multi-dimensional features such as user behavior, community structure, and information diffusion within social networks. Specifically, this research aims to construct a graph database model tailored for social network characteristics by introducing a node attribute weight adjustment mechanism to optimize the data storage structure. A hybrid algorithm combining random walk and depth-first search is designed for community detection. Additionally, a time-decay factor integrated information propagation path prediction model is proposed to effectively capture the patterns of information flow in social networks. Experimental results demonstrate that the constructed graph database model significantly improves query response time and storage space utilization compared to traditional methods. The hybrid algorithm accurately identifies meaningful community structures, while the information propagation path prediction model achieves a precision rate exceeding 85%. This research not only provides new technical approaches for social network data mining but also holds important theoretical value and application prospects in understanding the intrinsic mechanisms of social networks and preventing the spread of malicious information.
Keywords：Social Network Data Mining；Graph Database Model；Community Discovery Algorithm
目 录
引 言 1
第一章 图数据库与社交网络概述 2
1.1 图数据库基本原理 2
1.2 社交网络数据特点 2
1.3 图数据库在社交网络中的优势 3
第二章 社交网络图数据建模方法 5
2.1 数据模型选择依据 5
2.2 用户关系建模策略 5
2.3 信息传播路径建模 6
第三章 基于图数据库的挖掘算法 8
3.1 关系模式发现算法 8
3.2 社区结构识别方法 8
3.3 影响力传播分析技术 9
第四章 社交网络数据分析应用 11
4.1 用户行为模式分析 11
4.2 舆情监测与预警 11
4.3 精准营销推荐系统 12
结 论 13
参考文献 14
致 谢 15
