  Link prediction attempts to predict whether an unseen edge exists based on
only a portion of edges of a graph. A flurry of methods have been introduced in
recent years that attempt to make use of graph neural networks (GNNs) for this
task. Furthermore, new and diverse datasets have also been created to better
evaluate the effectiveness of these new models. However, multiple pitfalls
currently exist that hinder our ability to properly evaluate these new methods.
These pitfalls mainly include: (1) Lower than actual performance on multiple
baselines, (2) A lack of a unified data split and evaluation metric on some
datasets, and (3) An unrealistic evaluation setting that uses easy negative
samples. To overcome these challenges, we first conduct a fair comparison
across prominent methods and datasets, utilizing the same dataset and
hyperparameter search settings. We then create a more practical evaluation
setting based on a Heuristic Related Sampling Technique (HeaRT), which samples
hard negative samples via multiple heuristics. The new evaluation setting helps
promote new challenges and opportunities in link prediction by aligning the
evaluation with real-world situations. Our implementation and data are
available at https://github.com/Juanhui28/HeaRT

Machine Learning;Social and Information Networks