Filtering of complex systems using overlapping tree networks

Abstract

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.

6 pages, 4 figures

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Cited by in corpus (5)

• Analyzing and Modeling Real-World Phenomena with Complex Networks: A Survey of Applications
• Worldwide spreading of economic crisis
• Dynamic portfolio strategy using clustering approach
• Dynamic evolution of cross-correlations in the Chinese stock market
• Dynamic structure of stock communities: A comparative study between stock returns and turnover rates