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Social user geolocation based on K-medoids and Gaussian Kernel graph attention network.

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This study introduces a new method for social user geolocation, improving accuracy in rural areas. The KMKGAT model enhances location feature learning, outperforming existing techniques.

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Area of Science:

  • Computer Science
  • Data Science
  • Network Engineering

Background:

  • Accurate user location is vital for location-based services.
  • Current methods struggle with rural areas and ignore feature distances, reducing geolocation accuracy.
  • Social network data offers rich, yet complex, location-related information.

Purpose of the Study:

  • To develop a novel social user geolocation method (KMKGAT) that overcomes limitations of existing approaches.
  • To enhance the accuracy of user localization, particularly in challenging environments like rural areas.
  • To effectively integrate spatial proximity and textual features for improved geolocation.

Main Methods:

  • Proposed KMKGAT method combining k-medoids clustering and a Gaussian kernel graph attention network.
  • Utilized an anti-noise k-medoids algorithm for precise geographical user clustering.
  • Incorporated parameterized Gaussian kernel functions within the graph attention network to learn location-enhanced features.

Main Results:

  • KMKGAT demonstrated superior performance compared to state-of-the-art baseline methods.
  • The method effectively addressed issues of location feature deviation in social networks.
  • Experiments on three public Twitter datasets validated the proposed approach's effectiveness.

Conclusions:

  • The KMKGAT method offers a significant advancement in social user geolocation.
  • Accurate clustering and location-enhanced feature learning are key to improving geolocation accuracy.
  • This approach holds promise for more reliable location-based network services.