摘 要
随着社交网络的迅猛发展，海量用户行为数据的积累为数据分析与挖掘提供了重要基础。然而，如何高效存储、管理并深度挖掘这些数据，仍是当前研究的重要挑战。本研究旨在通过构建基于数据库的社交网络数据挖掘框架，提升数据处理效率与分析深度。本文提出一种结合关系型数据库与非关系型数据库的混合存储模型，以提高社交网络数据的存储灵活性与查询性能；同时，引入改进的社区发现算法与基于机器学习的兴趣预测模型，实现对用户关系与行为特征的深度挖掘。实验基于真实社交平台数据集，结果表明，所提方法在节点分类准确率上提升了8.6%，社区划分的模块度指标平均提高12.3%。本研究的主要贡献在于整合多源异构数据管理策略与优化挖掘算法，为社交网络分析提供了一套系统化的解决方案。研究成果可广泛应用于个性化推荐、舆情监控及社交网络治理等领域，具有较强的实践价值与理论意义。关键词：社交网络数据挖掘；混合存储模型；社区发现算法；兴趣预测模型；多源异构数据管理
ABSTRACT
With the rapid development of social networks, the accumulation of massive user behavior data has provided an important foundation for data analysis and mining. However, efficiently storing, managing, and deeply mining this data remains a significant challenge in current research. This study aims to enhance the efficiency of data processing and the depth of analysis by constructing a social network data mining fr amework based on databases. A hybrid storage model combining relational and non-relational databases is proposed to improve the flexibility of data storage and query performance in social networks. Additionally, an improved community detection algorithm and a machine learning-based interest prediction model are introduced to enable in-depth mining of user relationships and behavioral characteristics. Experiments are conducted using real-world social platform datasets, and the results demonstrate that the proposed approach improves node classification accuracy by 8.6% and increases the average modularity of community detection by 12.3%. The main contribution of this study lies in integrating multi-source heterogeneous data management strategies with optimized mining algorithms, thereby offering a systematic solution for social network analysis. The outcomes of this research can be widely applied to fields such as personalized recommendation systems, public opinion monitoring, and social network governance, holding both practical value and theoretical significance.Keywords: Social Network Data Mining; Hybrid Storage Model; Community Detection Algorithm; Interest Prediction Model; Multi-source Heterogeneous Data Management
目 录
摘 要 I
ABSTRACT II
绪 论 1
第一章 基于数据库的社交网络数据采集与存储 2
1.1 社交网络数据来源与类型分析 2
1.2 数据库在社交网络数据存储中的应用 2
1.3 大规模社交数据的高效入库策略 3
1.4 数据完整性与安全机制设计 3
第二章 基于数据库的社交网络数据预处理方法 4
2.1 数据清洗与去噪技术研究 4
2.2 用户行为数据的结构化转换 4
2.3 关系网络的建模与图数据库支持 5
2.4 数据特征提取与标准化处理 5
第三章 基于数据库的社交网络用户行为挖掘 6
3.1 用户活跃度与影响力评估模型 6
3.2 基于数据库的用户聚类分析方法 6
3.3 用户兴趣演化趋势挖掘 6
3.4 社群结构识别与动态变化分析 7
第四章 基于数据库的社交网络关系传播分析 8
4.1 信息扩散路径的数据库建模 8
4.2 基于图数据库的关系传播预测 8
4.3 情感倾向性与舆论演化分析 8
4.4 数据驱动下的社交影响力优化策略 9
结 论 10
参考文献 11
致 谢 12
