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Impacts of Data Splitting Strategies on Parameterized Link Prediction Algorithms

Xinshan Jiao,Yuxin Luo,Yilin Bi,Tao Zhou

Published 2025 in Unknown venue

ABSTRACT

Link prediction is a fundamental problem in network science, aiming to infer potential or missing links based on observed network structures. With the increasing adoption of parameterized models, the rigor of evaluation protocols has become critically important. However, a previously common practice of using the test set during hyperparameter tuning has led to human-induced information leakage, thereby inflating the reported model performance. To address this issue, this study introduces a novel evaluation metric, Loss Ratio, which quantitatively measures the extent of performance overestimation. We conduct large-scale experiments on 60 real-world networks across six domains. The results demonstrate that the information leakage leads to an average overestimation about 3.6\%, with the bias reaching over 15\% for specific algorithms. Meanwhile, heuristic and random-walk-based methods exhibit greater robustness and stability. The analysis uncovers a pervasive information leakage issue in link prediction evaluation and underscores the necessity of adopting standardized data splitting strategies to enable fair and reproducible benchmarking of link prediction models.

PUBLICATION RECORD

  • Publication year

    2025

  • Venue

    Unknown venue

  • Publication date

    2025-11-08

  • Fields of study

    Mathematics, Computer Science

  • Identifiers
    arXiv 2511.05834
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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