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VA-Index: Quantifying Assortativity Patterns in Networks with Multidimensional Nodal Attributes.

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Area of Science:

  • Network Science
  • Data Analysis
  • Statistical Modeling

Background:

  • Network connections often correlate with node attributes.
  • Existing metrics like the assortativity coefficient handle single-dimensional attributes.
  • Multi-dimensional node attributes, such as mobility patterns, require new quantification methods.

Purpose of the Study:

  • To develop a formal metric for quantifying network assortativity with respect to vector attributes.
  • To introduce the vector assortativity index (VA-index) as a novel approach.
  • To address the limitations of existing methods in handling multi-dimensional node features.

Main Methods:

  • Developed the vector assortativity index (VA-index).
  • Employed network randomization techniques.
  • Utilized empirical statistical hypothesis testing for validation.

Main Results:

  • The VA-index accurately quantifies assortativity for vector attributes.
  • Experimental results show the VA-index outperforms baseline extensions.
  • The VA-index demonstrates improved accuracy with increasing vector element variance.

Conclusions:

  • The VA-index provides a robust method for analyzing complex network structures.
  • This metric offers a significant advancement over traditional assortativity measures.
  • The VA-index is easily calibrated and effective for diverse network analysis applications.