多网络的组合分析
Combinatorial Analysis of Multiple Networks
摘要：
复杂网络的研究历来基于表示个体之间关系的简单图数据模型。然而，平面图模型通常无法准确捕捉现实。这导致了多层网络的发展。这些模型有可能成为网络数据分析的参考工具，但需要并行开发特定的分析方法，明确利用隐藏在层之间的信息和参考数据的临界质量的可用性，以实验的工具，并调查这些复杂系统的真实组织。在这项工作中，我们介绍了一个现实世界中的分层网络相结合的不同种类的在线和离线的关系，并提出了一种创新的方法和相关的分析工具，建议存在的隐藏图案遍历和相关的不同的表示层。我们还介绍了多个网络的介数中心性的概念。虽然报告了一些初步的实验证据，但我们的假设在很大程度上仍未得到验证，我们认为这需要新的分析方法和新的参考多层社交网络数据。
英文摘要：
The study of complex networks has been historically based on simple graph data models representing relationships between individuals. However, often reality cannot be accurately captured by a flat graph model. This has led to the development of multi-layer networks. These models have the potential of becoming the reference tools in network data analysis, but require the parallel development of specific analysis methods explicitly exploiting the information hidden in-between the layers and the availability of a critical mass of reference data to experiment with the tools and investigate the real-world organization of these complex systems. In this work we introduce a real-world layered network combining different kinds of online and offline relationships, and present an innovative methodology and related analysis tools suggesting the existence of hidden motifs traversing and correlating different representation layers. We also introduce a notion of betweenness centrality for multiple networks. While some preliminary experimental evidence is reported, our hypotheses are still largely unverified, and in our opinion this calls for the availability of new analysis methods but also new reference multi-layer social network data.
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