A Cooperative and Heuristic Community Detecting Algorithm
A Cooperative and Heuristic Community Detecting Algorithm
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DOI：
10.4304/jcp.7.1.135-140
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Rui-xin Ma;G. Deng;Xiao Wang
中科院分区：
文献类型：
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作者：
Rui-xin Ma;G. Deng;Xiao Wang
介绍了群体种子、向量和关系矩阵的概念。根据自由节点与现有社区之间的关系相似度，将节点划分为不同的组。提出了最小相似度阈值对节点进行过滤，给出了一种查找位于不同群落之间重叠区域节点的方法。本文通过对一系列网络数据集的分析，证明了该算法能够准确地将节点划分到具有高内聚和弱耦合的不同社区中。我们使用各种测试来说明我们的算法在检测人工和现实世界网络中的社会结构方面非常有效，并展示了如何使用它们来指导其他复杂系统。
This paper introduces the concept of community seed, vector and relation matrix. In terms of the relation similarity between free nodes and the existing communities, nodes are put into different groups. A minimum similarity threshold was proposed to filter the nodes, which gives a method to find the nodes who located at the overlapped area between different communities. This paper analyzed a series of network dataset for our algorithm and proved that it is able to accurately put nodes into different communities with high cohesion and weak coupling. We use a variety of test to illustrate that our algorithm is highly effective at detecting social structures in both artificial and real-world networks, and show how they can be used to give direction to other complex systems.