A tool for filtering information in complex systems

TLDR

The authors introduce a filtering technique that extracts a representative subgraph from complex data sets. The method extracts a subgraph of representative links, tunable by the graph genus, and for planar graphs (genus 0) generates triangular loops and four‑element cliques. When applied to 100 U.S.

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.