Multidimensional networks: foundations of structural analysis
Multidimensional networks: foundations of structural analysis
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DOI：
10.1007/s11280-012-0190-4
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发表时间：
2013-11-01
影响因子：
3.7
通讯作者：
Pedreschi, Dino
中科院分区：
文献类型：
--
作者：
Berlingerio, Michele;Coscia, Michele;Pedreschi, Dino
复杂网络受到科学界越来越多的关注，这也是由于现实世界网络数据的日益可用性。到目前为止，网络分析主要关注图的局部和全局属性的表征和度量，如直径、度分布、中心性等。近年来，人们指出了许多现实世界网络的多维性，即分析了许多包含任意对节点之间的多个连接的网络。尽管之前的研究已经认识到分析这种网络的重要性，但目前还没有一个完整的多维网络分析框架。这样的框架将使分析人员能够研究不同的现象，这些现象可以是对一维网络中发生的多维设置的概括，也可以是由多维度在真实网络中提供的额外复杂程度引起的新一类现象。本文的目的是给出多维网络分析的基础：我们提出了一个坚实的基本概念和分析措施，考虑到多维网络的一般结构。我们在不同的现实世界多维网络上测试了我们的框架，显示了所引入的措施的有效性和意义，这些措施能够从这些网络中提取有关复杂现象的重要和非随机信息。
Complex networks have been receiving increasing attention by the scientific community, thanks also to the increasing availability of real-world network data. So far, network analysis has focused on the characterization and measurement of local and global properties of graphs, such as diameter, degree distribution, centrality, and so on. In the last years, the multidimensional nature of many real world networks has been pointed out, i.e. many networks containing multiple connections between any pair of nodes have been analyzed. Despite the importance of analyzing this kind of networks was recognized by previous works, a complete framework for multidimensional network analysis is still missing. Such a framework would enable the analysts to study different phenomena, that can be either the generalization to the multidimensional setting of what happens in monodimensional networks, or a new class of phenomena induced by the additional degree of complexity that multidimensionality provides in real networks. The aim of this paper is then to give the basis for multidimensional network analysis: we present a solid repertoire of basic concepts and analytical measures, which take into account the general structure of multidimensional networks. We tested our framework on different real world multidimensional networks, showing the validity and the meaningfulness of the measures introduced, that are able to extract important and non-random information about complex phenomena in such networks.