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An egonet based approach to effective weighted network comparison.

Carlo Piccardi1

  • 1Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milano, Italy. carlo.piccardi@polimi.it.

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This study introduces new alignment-free metrics for comparing weighted networks, crucial for analyzing complex systems. These novel graph dissimilarity measures effectively classify network models and identify financial market anomalies.

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Area of Science:

  • Network Science
  • Data Analysis
  • Computational Science

Background:

  • Comparing graphs is essential in many fields, but methods for weighted networks are limited.
  • Existing metrics often ignore connection strength, potentially leading to inaccurate analysis.
  • Weighted network analysis requires specialized dissimilarity measures.

Purpose of the Study:

  • Introduce a novel family of alignment-free dissimilarity measures for undirected weighted networks.
  • Address the limitations of existing metrics for weighted graph comparison.
  • Provide tools for identifying network similarities and anomalies.

Main Methods:

  • Developed alignment-free metrics based on egonet feature distributions.
  • Metrics do not require node correspondence and can compare networks of different sizes.
  • Evaluated metrics on a testbed of weighted network models for classification tasks.

Main Results:

  • Proposed metrics achieve state-of-the-art performance in network model classification.
  • Demonstrated effectiveness in evaluating graph filtering schemes.
  • Successfully identified anomalies in stock market correlation graphs during financial instability.

Conclusions:

  • The new metrics offer a robust approach to comparing weighted networks.
  • Applicable to diverse tasks, including network filtering and anomaly detection in financial data.
  • Advance the field of network comparison with alignment-free, feature-distribution-based methods.