A tool for filtering information in complex systems
Date
2005
Authors
Tumminello, M
Aste, Tomaso
Di Matteo, Tiziana
Mantegna, R N
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Publisher
National Academy of Sciences (USA)
Abstract
We introduce a technique to filter out complex data sets by extracting a subgraph of representative links. Such a filtering can be tuned up to any desired level by controlling the genus of the resulting graph, We show that this technique is especially suitable for correlation-based graphs, giving filtered graphs that preserve the hierarchical organization of the minimum spanning tree but containing a larger amount of information in their internal structure. In particular in the case of planar filtered graphs (genus equal to 0), triangular loops and four-element cliques are formed. The application of this filtering procedure to 100 stocks in the U.S. equity markets shows that such loops and cliques have important and significant: relationships with the market structure and properties.
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Keywords: algorithm; article; cluster analysis; correlation analysis; mathematical analysis; mathematical model; priority journal Cluster analysis; Complex networks; Correlation analysis
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PNAS - Proceedings of the National Academy of Sciences of the United States of America
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Journal article
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2037-12-31
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