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Evaluating link prediction: New perspectives and recommendations

Created by
  • Haebom

Author

Bhargavi Kalyani I, A Rama Prasad Mathi, Niladri Sett

Outline

This paper addresses the limitations of previous studies on the evaluation of Link Prediction (LP) methods, an important problem in the fields of network science and machine learning, and proposes a more rigorous and controlled experimental setup. We address the issue that previous studies have been evaluated in a uniform setting without considering various factors such as network type, problem type, geodesic distance between nodes, characteristics and applicability of LP methods, and class imbalance. In this paper, we present an experimental setup that considers these factors and conduct extensive experiments using various real-world network datasets. Based on the experimental results, we provide insights into the interactions of factors that affect the performance of LP methods and suggest best practices for evaluating LP methods.

Takeaways, Limitations

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Takeaways:
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By presenting a more rigorous and controlled experimental setup for evaluating link prediction methods, we can overcome the limitations of existing studies and obtain more reliable results.
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We systematically analyze the impact of various factors (network type, problem type, geodesic distance, characteristics of LP method, class imbalance, etc.) on link prediction performance and provide insights into their interactions.
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Provides best practices for evaluating link prediction methods to increase reproducibility and reliability of research.
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Limitations:
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Further research is needed to determine whether the presented experimental setup and best practices can be fully applied to all types of network and link prediction problems.
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In addition to the factors considered in this paper, there may be other factors that may affect link prediction performance.
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Results may be limited by the nature of the dataset used. Additional experiments using different types of datasets are needed.
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