Thakur G S, Tiwari R, Thai M T, Chen S-S, Dress A W M
University of Florida, CISE, Gainesville, FL, USA.
IET Syst Biol. 2009 Jul;3(4):266-78. doi: 10.1049/iet-syb.2007.0061.
Identification of interaction patterns in complex networks via community structures has gathered a lot of attention in recent research studies. Local community structures provide a better measure to understand and visualise the nature of interaction when the global knowledge of networks is unknown. Recent research on local community structures, however, lacks the feature to adjust itself in the dynamic networks and heavily depends on the source vertex position. In this study the authors propose a novel approach to identify local communities based on iterative agglomeration and local optimisation. The proposed solution has two significant improvements: (i) in each iteration, agglomeration strengthens the local community measure by selecting the best possible set of vertices, and (ii) the proposed vertex and community rank criterion are suitable for the dynamic networks where the interactions among vertices may change over time. In order to evaluate the proposed algorithm, extensive experiments and benchmarking on computer generated networks as well as real-world social and biological networks have been conducted. The experiment results reflect that the proposed algorithm can identify local communities, irrespective of the source vertex position, with more than 92% accuracy in the synthetic as well as in the real-world networks.
通过社区结构识别复杂网络中的交互模式在最近的研究中受到了广泛关注。当网络的全局知识未知时，局部社区结构为理解和可视化交互性质提供了更好的方法。然而，最近关于局部社区结构的研究缺乏在动态网络中自我调整的功能，并且严重依赖于源顶点位置。在本研究中，作者提出了一种基于迭代凝聚和局部优化来识别局部社区的新方法。所提出的解决方案有两个显著改进：（i）在每次迭代中，凝聚通过选择最佳的顶点集来强化局部社区度量；（ii）所提出的顶点和社区排名标准适用于顶点之间的交互可能随时间变化的动态网络。为了评估所提出的算法，已经在计算机生成的网络以及真实世界的社会和生物网络上进行了广泛的实验和基准测试。实验结果表明，所提出的算法能够识别局部社区，而与源顶点位置无关，在合成网络和真实世界网络中的准确率均超过92%。
