利用重叠树网络对复杂系统进行滤波
Filtering of complex systems using overlapping tree networks
摘要：
我们介绍了一种技术，该技术能够通过将复杂系统映射到网络并提取具有最强链接的子图来从复杂系统中过滤出信息。这个想法是基于最小生成树，它可以应用于图的集合，这些图具有系统元素之间的不同交互集作为链接，这些元素被描述为网络节点。它也可以应用于基于相关性的图，其中链接被加权并表示所有节点对之间的相关强度。我们将这种方法应用于欧洲科学合作网络，该网络由欧洲框架计划FP6支持的所有项目组成，并且还应用于纽约证券交易所交易的100只最高资本化股票的相关性网络。对于这两种情况下，我们确定了有意义的结构，如一个强大的相互联系的社区的国家在合作网络中发挥重要作用，和集群的股票属于不同部门的经济活动，这给了重要的信息调查系统。
英文摘要：
We introduce a technique that is capable to filter out information from complex systems, by mapping them to networks, and extracting a subgraph with the strongest links. This idea is based on the Minimum Spanning Tree, and it can be applied to sets of graphs that have as links different sets of interactions among the system's elements, which are described as network nodes. It can also be applied to correlation-based graphs, where the links are weighted and represent the correlation strength between all pairs of nodes. We applied this method to the European scientific collaboration network, which is composed of all the projects supported by the European Framework Program FP6, and also to the correlation-based network of the 100 highest capitalized stocks traded in the NYSE. For both cases we identified meaningful structures, such as a strongly interconnected community of countries that play important role in the collaboration network, and clusters of stocks belonging to different sectors of economic activity, which gives significant information about the investigated systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
