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Generating attributed networks with communities.

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This study introduces a novel synthetic generator for attributed networks with community structure. It reliably models real-world network properties, addressing limitations of existing generators.

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Area of Science:

  • Network Science
  • Data Mining
  • Computer Science

Background:

  • Modern applications often utilize attributed networks, where nodes possess attributes and relationships form information networks.
  • Discovering community structures within these networks is crucial for various applications.
  • Existing synthetic network generators often neglect attribute values, focusing primarily on connectivity.

Purpose of the Study:

  • To propose a new synthetic generator for attributed networks with community structure.
  • To create a generator that accurately reflects real-world network properties, including attribute values.
  • To provide a reliable tool for evaluating and comparing community discovery algorithms.

Main Methods:

  • Development of a novel synthetic network generation algorithm.
  • Incorporation of attribute values and their relationship to network structure.
  • Validation against properties observed in real-world attributed networks.

Main Results:

  • The proposed generator successfully creates attributed networks with inherent community structures.
  • The generated networks exhibit properties consistent with real-world data, considering both connectivity and attributes.
  • The generator overcomes the limitations of previous methods by integrating attribute-based community characteristics.

Conclusions:

  • The new generator offers a significant advancement for creating realistic synthetic attributed networks.
  • It provides a valuable resource for the rigorous evaluation of community detection algorithms.
  • This work facilitates more accurate benchmarking and development in network analysis research.