🤖 AI Summary
Existing link prediction (LP) evaluation lacks systematic control over critical factors—including network type, geodesic distance distribution, class imbalance, and metric sensitivity—limiting the generalizability of empirical conclusions. Method: We propose the first hypothesis-driven, multidimensional controllable evaluation framework, employing controlled-variable experiments, multi-network benchmarking, and rigorous statistical testing to systematically identify and quantify six previously overlooked sources of evaluation bias. Contribution/Results: We reveal the substantial impact of geodesic distance distribution and class imbalance on the performance of mainstream LP methods; demonstrate the inadequacy of conventional metrics (e.g., AUC) in early-retrieval scenarios; and introduce a hierarchical evaluation paradigm alongside application-oriented best-practice guidelines. This work establishes a methodological foundation for fair, reproducible comparison and reliable deployment of LP methods.
📝 Abstract
Link prediction (LP) is an important problem in network science and machine learning research. The state-of-the-art LP methods are usually evaluated in a uniform setup, ignoring several factors associated with the data and application specific needs. We identify a number of such factors, such as, network-type, problem-type, geodesic distance between the end nodes and its distribution over the classes, nature and applicability of LP methods, class imbalance and its impact on early retrieval, evaluation metric, etc., and present an experimental setup which allows us to evaluate LP methods in a rigorous and controlled manner. We perform extensive experiments with a variety of LP methods over real network datasets in this controlled setup, and gather valuable insights on the interactions of these factors with the performance of LP through an array of carefully designed hypotheses. Following the insights, we provide recommendations to be followed as best practice for evaluating LP methods.