基于核心节点的动态重叠社区发现

Yan Liu, Hong Yu
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引用次数: 0

摘要

现实世界中的社交网络是不断进化和大规模的。群落结构检测可以有效地表达复杂网络的结构和特征。许多经典的增量聚类和进化聚类算法被提出来检测动态网络中的群落。然而,这些算法很少考虑节点的重要性,在检测过程中不同社区之间的重叠。本文提出了一种基于核心节点的算法,该算法不仅可以检测动态重叠社区,还可以跟踪网络社区的演变。同时,引入了一种用集合对来描述重叠群体的三向表示方法。在实际数据集上的实验结果表明,本文提出的方法优于动态社区检测算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dynamic Overlapping Community Discovery Based on Core Nodes
Social networks in the real world are evolutionary and large scale. Detecting the community structure could express the structure and characteristics of complex networks effectively. Many classic incremental clustering and evolutionary clustering algorithms have been proposed to detect the communities in dynamic networks. However, these algorithms rare to consider the importance of nodes, the overlap between different communities during the process of detection. In this paper, an algorithm based on core nodes was proposed which could not only detect dynamic overlapping communities, but also trace the evolution of network communities. Meanwhile, a three-way representation of a community by a pair of sets is introduced to describe the overlapping communities. Experiment results on real-world data sets demonstrate that our proposed method performs better than the well-known dynamic community detection algorithm.
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