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Area of Science:

  • Network Science
  • Graph Theory
  • Sociology

Background:

  • Multiplex networks, comprising multiple layers of relationships, are common in real-world systems.
  • Understanding local network structures (graphlets) is crucial for analyzing complex systems.
  • Existing graphlet analysis methods are often limited to single-layer networks.

Purpose of the Study:

  • To extend graphlet analysis to multiplex, multilayer, and multilevel networks.
  • To analyze the structural differences between economic trade and social networks using graphlet analysis.
  • To investigate the presence and significance of strong ties in social networks through graphlet patterns.

Main Methods:

  • Development of graphlet analysis techniques for multiplex networks.
  • Application of the analysis to a 957-plex economic trade network dataset.
  • Application of the analysis to 75 multiplex social network datasets (12-plex each).

Main Results:

  • Economic networks exhibit a higher frequency of wedges (open triads) compared to social networks.
  • Social networks are dominated by triangles (closed triads), indicating strong tie clustering.
  • Contrary to prior research, wedges composed solely of strong ties were found to be present and correlated in the analyzed social networks.

Conclusions:

  • Graphlet analysis provides a powerful tool for differentiating network structures across various domains.
  • The findings offer new insights into economic interdependence and social tie strength.
  • The study challenges established theories on the role of strong and weak ties in social structures.