摘要翻译:
自然界、社会和技术中的许多网络具有中观层次的组织特征,由一组节点组成紧密相连的单元,称为社区或模块,这些单元之间只有微弱的联系。揭示这种社团结构是复杂网络领域中最重要的问题之一。网络通常表现为一种等级组织,社区嵌入其他社区;而且,节点可以在不同的社区之间共享。在这里,我们提出了第一个算法,发现重叠社区和层次结构。该方法基于适应度函数的局部优化。群落结构由适应度直方图中的峰值显示。可以通过一个参数来调整分辨率,从而能够调查组织的不同层次。在真实网络和人工网络上的测试都给出了很好的结果。
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英文标题:
《Detecting the overlapping and hierarchical community structure of
complex networks》
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作者:
Andrea Lancichinetti, Santo Fortunato, Janos Kertesz
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最新提交年份:
2009
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分类信息:
一级分类:Physics 物理学
二级分类:Physics and Society 物理学与社会
分类描述:Structure, dynamics and collective behavior of societies and groups (human or otherwise). Quantitative analysis of social networks and other complex networks. Physics and engineering of infrastructure and systems of broad societal impact (e.g., energy grids, transportation networks).
社会和团体(人类或其他)的结构、动态和集体行为。社会网络和其他复杂网络的定量分析。具有广泛社会影响的基础设施和系统(如能源网、运输网络)的物理和工程。
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一级分类:Physics 物理学
二级分类:Statistical Mechanics 统计力学
分类描述:Phase transitions, thermodynamics, field theory, non-equilibrium phenomena, renormalization group and scaling, integrable models, turbulence
相变,热力学,场论,非平衡现象,重整化群和标度,可积模型,湍流
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一级分类:Physics 物理学
二级分类:Computational Physics 计算物理学
分类描述:All aspects of computational science applied to physics.
应用于物理学的计算科学的各个方面。
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一级分类:Statistics 统计学
二级分类:Computation 计算
分类描述:Algorithms, Simulation, Visualization
算法、模拟、可视化
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英文摘要:
Many networks in nature, society and technology are characterized by a mesoscopic level of organization, with groups of nodes forming tightly connected units, called communities or modules, that are only weakly linked to each other. Uncovering this community structure is one of the most important problems in the field of complex networks. Networks often show a hierarchical organization, with communities embedded within other communities; moreover, nodes can be shared between different communities. Here we present the first algorithm that finds both overlapping communities and the hierarchical structure. The method is based on the local optimization of a fitness function. Community structure is revealed by peaks in the fitness histogram. The resolution can be tuned by a parameter enabling to investigate different hierarchical levels of organization. Tests on real and artificial networks give excellent results.
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PDF链接:
https://arxiv.org/pdf/802.1218