[论文解读] A Real-Time Detecting Algorithm for Tracking Community Structure of Dynamic Networks

Jiaxing Shang, Lianchen Liu+5 more|arXiv (Cornell University)|Jul 10, 2014

Complex Network Analysis Techniques参考文献 27被引用 41

一句话总结

本文提出了一种计算复杂度低的实时增量算法，用于在动态网络中追踪社区结构。通过使用Blondel等人提出的社区检测方法进行初始化，并应用高效的更新规则，该算法能够长时间保持高模块度，优于CNM算法在Enron邮件网络及另外三个真实世界数据集上的表现。

ABSTRACT

In this paper a simple but efficient real-time detecting algorithm is proposed for tracking community structure of dynamic networks. Community structure is intuitively characterized as divisions of network nodes into subgroups, within which nodes are densely connected while between which they are sparsely connected. To evaluate the quality of community structure of a network, a metric called modularity is proposed and many algorithms are developed on optimizing it. However, most of the modularity based algorithms deal with static networks and cannot be performed frequently, due to their high computing complexity. In order to track the community structure of dynamic networks in a fine-grained way, we propose a modularity based algorithm that is incremental and has very low computing complexity. In our algorithm we adopt a two-step approach. Firstly we apply the algorithm of Blondel et al for detecting static communities to obtain an initial community structure. Then, apply our incremental updating strategies to track the dynamic communities. The performance of our algorithm is measured in terms of the modularity. We test the algorithm on tracking community structure of Enron Email and three other real world datasets. The experimental results show that our algorithm can keep track of community structure in time and outperform the well known CNM algorithm in terms of modularity.

研究动机与目标

• 为高效追踪动态网络中的社区结构提供解决方案。
• 与传统的基于模块度的静态网络算法相比，降低计算复杂度。
• 实现实时、细粒度的社区结构更新，以适应网络的演化。
• 在无需完整重新计算的情况下，维持高模块度。

提出的方法

• 使用Blondel等人提出的快速贪心算法检测静态网络中的初始社区结构。
• 应用增量更新策略，以适应新增边或节点后的社区变化。
• 通过仅关注受影响的节点和社区，避免完整重新计算。
• 通过局部模块度优化指导社区更新，以保持整体结构。
• 该方法设计为计算轻量级，适合实时部署。
• 使用模块度作为主要指标，用于评估和引导社区更新。

实验结果

研究问题

• RQ1 低复杂度算法是否能够在追踪动态网络中演化的社区结构时，保持高模块度？
• RQ2 与完整重新计算相比，增量更新策略在模块度和效率方面表现如何？
• RQ3 该算法是否能够在真实世界动态网络中实现实时社区演化追踪？
• RQ4 所提出的方法是否在模块度和计算成本方面均优于成熟的算法（如CNM）？

主要发现

• 在Enron邮件数据集上，所提算法的模块度高于CNM算法。
• 该算法在所有测试的动态网络数据集中均保持了高模块度，且计算开销极低。
• 增量更新机制实现了无需完整重新处理网络的实时追踪。
• 该方法具有良好的可扩展性，适用于大规模动态网络。
• 实验结果证实了该算法能够以细粒度的时间分辨率追踪社区演化。