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首页> 外文期刊>International journal of data mining, modelling and management >Efficiently mining community structures in weighted social networks
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Efficiently mining community structures in weighted social networks

机译：在加权社交网络中有效挖掘社区结构

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In the literature, there are several models for detecting communities in social networks. In Zardi and Romdhane (2013), we presented a robust method, called maximum equilibrium purity (MEP), in which we defined a new function that qualifies a network partition into communities, and we presented an algorithm that optimises this function. We proved that, unlike modularity-based models, MEP does not suffer from the 'resolution limit' problem. However, MEP operates only on unweighted networks; i.e., networks where all connections are considered equally. Hence, strengths of social ties between network nodes are ignored. Unfortunately, this assumption may not hold in several real-world networks where tie strengths play a major role. In this paper, we present the maximum weighted equilibrium purity algorithm (MWEP), the extension of MEP to weighted networks. Like the original model, the extended model is proved to circumvent the 'resolution limit' problem encountered in community detection. In addition, we have applied our model to real-world and synthetic social networks and experimental results are more than encouraging.
机译：在文献中，有几种检测社交网络中社区的模型。在Zardi和Romdhane（2013）中，我们提出了一种稳健的方法，称为最大平衡纯度（MEP），在其中定义了一个新功能，该功能可将网络划分为多个社区，并提供一种算法来优化此功能。我们证明，与基于模块化的模型不同，MEP不会遭受“分辨率极限”问题的困扰。但是，MEP仅在未加权的网络上运行。即所有连接均被平等考虑的网络。因此，网络节点之间的社会纽带优势被忽略了。不幸的是，这种假设可能在领带强度起主要作用的多个现实网络中不成立。在本文中，我们提出了最大加权平衡纯度算法（MWEP），将MEP扩展为加权网络。与原始模型一样，扩展模型被证明可以避免社区检测中遇到的“分辨率极限”问题。此外，我们已将模型应用于现实世界和综合性社交网络，实验结果令人鼓舞。

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