摘要
随着信息技术的迅猛发展，数据量呈指数级增长，传统关系型数据库在处理复杂关联数据时面临诸多挑战。图数据库以其独特的图结构存储和查询方式，在表达实体间复杂关系方面展现出显著优势。本文聚焦图数据库技术原理及其在复杂网络分析中的应用，旨在深入探讨图数据库的核心机制，包括图模型构建、图算法优化以及高效索引策略等关键技术，并结合实际应用场景进行系统研究。创新性地提出了一种基于图嵌入的混合索引方法，有效提升了大规模图数据的检索速度与准确性。此外，针对复杂网络中常见的动态更新问题，设计了增量式图算法框架，实现了对实时变化数据的高效处理。研究表明，图数据库技术为复杂网络分析提供了全新的解决方案，不仅能够满足海量关联数据的高效管理需求，还为相关领域的研究与应用开辟了新的思路和方向。
关键词：图数据库；复杂网络分析；图嵌入混合索引；增量式图算法；查询效率优化
Abstract
With the rapid development of information technology, the amount of data increases exponentially, and traditional relational databases face many challenges when dealing with complex relational data. Graph database, with its unique graph structure storage and query, has shown remarkable advantages in expressing complex relationships among entities. This paper focuses on the principle of graph database technology and its application in complex network analysis, aiming to deeply explore the core mechanism of graph database, including key technologies such as graph model construction, graph algorithm optimization and efficient index strategy, and conduct systematic research combined with practical application scenarios. A novel hybrid index method based on graph embedding is proposed, which effectively improves the retrieval speed and accuracy of large-scale graph data. In addition, an incremental graph algorithm fr amework is designed to solve the dynamic update problem in complex networks, which realizes the efficient processing of real-time changing data. The research shows that graph database technology provides a new solution for complex network analysis, which can not only meet the needs of efficient management of massive associated data, but also open up new ideas and directions for research and application in related fields.
Keywords：Graph Database; Complex Network Analysis; Graph Embedding Hybrid Index; Incremental Graph Algorithm; Query Efficiency Optimization
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
二、图数据库技术原理 2
(一) 图数据库基本概念 2
(二) 图数据存储机制 2
(三) 图查询语言及其优化 3
三、复杂网络分析基础 3
(一) 复杂网络特征定义 3
(二) 关键节点识别方法 4
(三) 社区发现算法研究 5
四、图数据库在复杂网络中的应用 5
(一) 社交网络关系挖掘 5
(二) 生物网络路径分析 6
(三) 金融风险传播建模 7
结 论 8
参考文献 9
