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Nowadays, link prediction has attracted significant attentions from the research community. It is a crucial task in social network analysis (SNA) that involves identifying missing links and predicting new links in the network. In recent years, several researchers have proposed solutions to tackle this issue. However, a comprehensive review of the key contributions is still needed to enable an in-depth analysis. There are numerous ways for link prediction, including similarity-based, supervised, and deep learning-based models. This study reviews recent link prediction methods and classifies them according to their model types. By creating a systematic categorization for proposed algorithms, chosen issues, and evaluation measures along with selected network datasets, a thorough study is offered. Finally, we discuss link prediction applications to round up the survey.

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Link Prediction in Social Networks: A Literature Review

  • 会议论文

  • 发表日期：

    2026年

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  • Mohamed Badiy,
  • Charaf Hamidi,
  • Fatima Amounas,
  • Mourade Azrour,
  • Salma Bendaoud

摘要

Nowadays, link prediction has attracted significant attentions from the research community. It is a crucial task in social network analysis (SNA) that involves identifying missing links and predicting new links in the network. In recent years, several researchers have proposed solutions to tackle this issue. However, a comprehensive review of the key contributions is still needed to enable an in-depth analysis. There are numerous ways for link prediction, including similarity-based, supervised, and deep learning-based models. This study reviews recent link prediction methods and classifies them according to their model types. By creating a systematic categorization for proposed algorithms, chosen issues, and evaluation measures along with selected network datasets, a thorough study is offered. Finally, we discuss link prediction applications to round up the survey.