摘 要
社交网络的快速发展催生了对社区结构分析的迫切需求，而图算法作为揭示网络中隐藏社区的关键工具，其计算效率直接影响分析效果。然而，随着社交网络规模的指数级增长，传统串行图算法在处理大规模数据时面临显著性能瓶颈。为此，本文旨在研究并提出一种高效的并行化加速策略，以提升图算法在社交网络社区发现中的计算性能。具体而言，本文基于现代并行计算架构设计了一种分层分区与任务调度相结合的优化方法，通过减少通信开销和负载均衡实现性能提升。本文提出的动态负载均衡机制有效缓解了因数据分布不均导致的性能下降问题。研究的主要贡献在于提供了一种普适性强且易于扩展的并行化框架，为大规模社交网络社区发现任务提供了新的解决思路，同时为相关领域的高性能计算研究奠定了基础。
关键词：社交网络社区发现；并行化加速策略；分层分区优化；动态负载均衡；高性能计算
Parallel Acceleration Strategies for Graph Algorithms in Community Detection of Social Networks
英文人名
Directive teacher:×××
Abstract
The rapid development of social networks has brought about an urgent need for community structure analysis, and graph algorithm, as a key tool to reveal hidden communities in networks, has a direct impact on its computational efficiency. However, with the exponential growth of the scale of social networks, traditional serial graph algorithms face significant performance bottlenecks when processing large-scale data. Therefore, this paper aims to study and propose an efficient parallelization acceleration strategy to improve the computational performance of graph algorithms in social network community discovery. Specifically, based on modern parallel computing architecture, this paper designs an optimization method combining hierarchical partitioning and task scheduling, which improves performance by reducing communication overhead and load balancing. The dynamic load balancing mechanism proposed in this paper effectively alleviates the performance degradation caused by uneven data distribution. The main contribution of this study is to provide a universal and easily extensible parallelization fr amework, which provides a new solution for the large-scale social network community discovery task, and lays a foundation for the high-performance computing research in related fields.
Keywords: Social Network Community Detection；Parallel Acceleration Strategy；Hierarchical Partition Optimization；Dynamic Load Balancing；High Performance Computing
目 录
引言 1
一、社交网络与图算法基础 1
(一)社交网络特性分析 1
(二)图算法在社区发现中的应用 1
(三)并行化加速的必要性 2
二、并行化加速策略设计 2
(一)并行计算模型选择 2
(二)数据划分与负载均衡 3
(三)算法优化与性能评估 4
三、典型图算法的并行实现 4
(一)基于标签传播的并行算法 4
(二)模块度优化的并行方法 5
(三)层次聚类的并行加速技术 5
四、实验验证与结果分析 6
(一)实验环境与数据集介绍 6
(二)性能对比与加速效果分析 7
(三)可扩展性与局限性讨论 7
结论 8
参考文献 9
致谢 9
