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Patterns in Temporal Networks with Higher-Order Egocentric Structures.

Beatriz Arregui-García1, Antonio Longa2, Quintino Francesco Lotito2

  • 1Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB, 07122 Palma de Mallorca, Spain.

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|March 28, 2024
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Summary
This summary is machine-generated.

Analyzing complex social dynamics requires new methods. We introduce hyper egocentric temporal neighborhoods to capture group interactions, revealing that higher-order structures significantly explain variability in temporal network data.

Keywords:
motifssocial interactionstemporal hypergraphs

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Area of Science:

  • Complex Systems Science
  • Network Science
  • Social Network Analysis

Background:

  • Temporal networks model time-evolving interactions but often focus on pairwise relationships.
  • Existing egocentric temporal neighborhood measures neglect group interactions common in social systems.
  • Higher-order networks offer a framework for analyzing group interactions.

Purpose of the Study:

  • To generalize egocentric temporal neighborhood analysis to higher-order interactions using hypergraphs.
  • To introduce the concept of "hyper egocentric temporal neighborhoods" for analyzing complex social dynamics.
  • To assess the impact of higher-order interactions on temporal network analysis.

Main Methods:

  • Generalizing temporal networks to hypergraphs to represent group interactions.
  • Defining and applying "hyper egocentric temporal neighborhoods" for network decomposition.
  • Analyzing temporal network data, including second-order interactions (triplets).

Main Results:

  • The proposed hyper egocentric temporal neighborhoods effectively capture higher-order social interactions.
  • Second-order structures (triplets) account for the majority of variability in the data.
  • This variability is observed across different datasets, between nodes, and over time.

Conclusions:

  • Higher-order representations are crucial for a comprehensive understanding of temporal social networks.
  • Hyper egocentric temporal neighborhoods provide a powerful tool for analyzing complex, group-based interactions.
  • The findings highlight the importance of moving beyond pairwise analysis in complex systems.