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A Semantic-Enhancement-Based Social Network User-Alignment Algorithm.

Yuanhao Huang1, Pengcheng Zhao1, Qi Zhang2

  • 1The College of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China.

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|January 21, 2023
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Summary
This summary is machine-generated.

This study introduces SENUA, a novel algorithm for user alignment across social networks. By reducing semantic gaps using user attributes, content, and check-ins, SENUA significantly improves alignment accuracy.

Keywords:
graph contrastive learningsemantic enhancementsocial networksuser alignment

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Data Mining

Background:

  • User alignment, associating multiple social network accounts for the same individual, is crucial but challenged by diverse user behaviors across platforms.
  • Existing methods struggle with accuracy due to the semantic gap between a user's presence on different social networks.

Purpose of the Study:

  • To enhance the accuracy of user alignment by bridging the semantic gap between users' profiles on different social networks.
  • To propose a semantically enhanced social network user alignment algorithm (SENUA) that leverages multi-faceted user data.

Main Methods:

  • SENUA utilizes user attributes, user-generated content (UGC), and check-in data to mine semantic features and reduce local semantic noise.
  • Multi-view graph-data augmentation enhances the algorithm's robustness to noise.
  • Multi-headed graph attention networks and multi-view contrastive learning optimize embedding vectors to strengthen similarities among aligned users.

Main Results:

  • Experimental results demonstrate an average improvement of 6.27% over baseline methods at hit-precision30.
  • The proposed SENUA algorithm shows significant gains in user alignment accuracy.

Conclusions:

  • Semantic enhancement effectively improves the accuracy of social network user alignment.
  • SENUA offers a robust and accurate approach to user alignment by addressing semantic discrepancies and optimizing user representations.