Detecting network communities beyond assortativity-related attributes
Detecting network communities beyond assortativity-related attributes
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DOI：
10.1103/physreve.90.012806
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发表时间：
2014-07
期刊：
影响因子：
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通讯作者：
Xin Liu;T. Murata;Ken Wakita
中科院分区：
文献类型：
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作者：
Xin Liu;T. Murata;Ken Wakita
在网络科学中，类比性是指具有相似属性的节点之间存在链接的趋势。例如，在社交网络中，年龄、国籍、地点、种族、收入、教育水平、宗教信仰和语言相似的个人之间往往存在联系。因此，各种属性共同影响网络拓扑。一个有趣的问题是发现特定类别相关属性ρ之外的社区结构，即剔除ρ对网络拓扑的影响，揭示由于其他属性而隐藏的社区结构。解决这一问题的一种方法是重新定义模块化度量的零模型，以模拟ρ对网络拓扑的影响。然而，一个挑战是，我们不知道ρ和其他属性对网络拓扑的影响程度。在本文中，我们提出了一个距离模数，它允许我们自由地选择任何合适的函数来模拟ρ的效果。这种自由可以帮助我们探测ρ的影响，并发现由于其他属性而隐藏的社区。我们在合成基准和两个真实网络上测试了距离模块化的有效性。
In network science, assortativity refers to the tendency of links to exist between nodes with similar attributes. In social networks, for example, links tend to exist between individuals of similar age, nationality, location, race, income, educational level, religious belief, and language. Thus, various attributes jointly affect the network topology. An interesting problem is to detect community structure beyond some specific assortativity-related attributes ρ, i.e., to take out the effect of ρ on network topology and reveal the hidden community structures which are due to other attributes. An approach to this problem is to redefine the null model of the modularity measure, so as to simulate the effect of ρ on network topology. However, a challenge is that we do not know to what extent the network topology is affected by ρ and by other attributes. In this paper, we propose a distance modularity, which allows us to freely choose any suitable function to simulate the effect of ρ. Such freedom can help us probe the effect of ρ and detect the hidden communities which are due to other attributes. We test the effectiveness of distance modularity on synthetic benchmarks and two real-world networks.