Abstract
This paper provides a comprehensive overview and evaluation of link prediction techniques. The study includes an analysis of various methods, ranging from simple heuristics to complex embedding-based approaches. The comparative study evaluates the performance of each technique across a range of diverse data sets, and offers unique insights into the strengths and limitations of each approach, as well as their suitability for different types of network structure. For example, the research shows that, while some techniques may perform well on small and sparse networks, they may not be as effective on larger, denser networks. By providing a thorough analysis of various link prediction techniques, this study proffers a valuable resource for researchers seeking to develop more effective algorithms for predicting links in networks. The findings of this study contribute to a deeper understanding of the dynamics and structure of networks.
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