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SCOUT: simultaneous time segmentation and community detection in dynamic networks.

Yuriy Hulovatyy1, Tijana Milenković1

  • 1Department of Computer Science and Engineering, Eck Institute for Global Health, and Interdisciplinary Center for Network Science and Applications (iCeNSA); University of Notre Dame, Notre Dame, IN 46556, USA.

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This study introduces SCOUT, a new framework for segment community detection in dynamic networks. SCOUT effectively identifies time segments and their community structures, outperforming existing methods in accuracy and efficiency.

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Area of Science:

  • Complex Systems Science
  • Network Science
  • Computational Biology

Background:

  • Dynamic networks model evolving real-world systems.
  • Community detection in networks identifies related node groups.
  • Existing methods assume static or fully dynamic communities, missing intermediate cases.

Purpose of the Study:

  • To develop a method for segment community detection (SCD) in dynamic networks.
  • To simultaneously identify time segments and their community structures.
  • To address limitations of methods focusing on only segmentation or partition quality.

Main Methods:

  • Formulated the combined problem of segment community detection (SCD).
  • Introduced SCOUT, an optimization framework balancing segmentation and partition quality.
  • Evaluated SCOUT against existing methods on benchmark and biological network data.

Main Results:

  • SCOUT significantly outperforms existing methods in accuracy for SCD.
  • SCOUT demonstrates superior computational efficiency compared to adapted methods.
  • The framework successfully identified community structures in human aging biological networks.

Conclusions:

  • SCOUT provides an effective solution for segment community detection in dynamic networks.
  • The framework offers a more realistic approach by considering both segmentation and partition quality.
  • SCOUT has potential applications in analyzing complex biological systems like human aging.