Finding missing edges in networks based on their community structure
Finding missing edges in networks based on their community structure
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DOI：
10.1103/physreve.85.056112
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发表时间：
2011-09
期刊：
影响因子：
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通讯作者：
B. Yan;Steve Gregory
中科院分区：
文献类型：
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作者：
B. Yan;Steve Gregory
基于不完全网络结构的各种局部或全局性质，已经提出了许多边缘预测方法。社区结构是网络的另一个重要特征：社区中的顶点连接比平均水平更紧密。同一社区中的顶点通常具有“相似”的属性，这表明丢失的边更有可能在社区中找到。我们利用这一洞察力，提出了一个战略，结合现有的边缘预测方法与社区检测的边缘预测。我们表明，这种方法提供了更好的预测精度比现有的边缘预测方法单独。
Many edge prediction methods have been proposed, based on various local or global properties of the structure of an incomplete network. Community structure is another significant feature of networks: Vertices in a community are more densely connected than average. It is often true that vertices in the same community have "similar" properties, which suggests that missing edges are more likely to be found within communities than elsewhere. We use this insight to propose a strategy for edge prediction that combines existing edge prediction methods with community detection. We show that this method gives better prediction accuracy than existing edge prediction methods alone.