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Evaluating link prediction: New perspectives and recommendations

arXiv 2502.12777 ↗First posted 18 Feb 2025cs.SI cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 66 references that are in the map. First posted on arXiv 18 Feb 2025.

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References · 18

  1. 2017Inductive Representation Learning on Large Graphscited by 6,320
  2. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,474
  3. 2016node2vec: Scalable Feature Learning for Networkscited by 1,918
  4. 2014DeepWalk: Online Learning of Social Representationscited by 1,547
  5. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,139
  6. 2018Link Prediction Based on Graph Neural Networkscited by 832
  7. 2019Predicting Dynamic Embedding Trajectory in Temporal Interaction Networkscited by 448
  8. 2022Towards Better Evaluation for Dynamic Link Predictioncited by 122
  9. 2016Motifs in Temporal Networkscited by 101
  10. 2021Neural Link Prediction with Walk Poolingcited by 37
  11. 2015Evaluating Link Prediction Methodscited by 31
  12. 2022Bring Your Own View: Graph Neural Networks for Link Prediction with Personalized Subgraph Selectioncited by 26
6 more
  1. 2014Link Prediction in Social Networks: the State-of-the-Artcited by 22
  2. 2022Temporal Link Prediction: A Unified Framework, Taxonomy, and Reviewcited by 20
  3. 2016Evaluating Link Prediction Accuracy on Dynamic Networks with Added and Removed Edgescited by 8
  4. 2021Collaborative Filtering Approach to Link Predictioncited by 4
  5. 2024Link Prediction Accuracy on Real-World Networks Under Non-Uniform Missing Edge Patternscited by 2
  6. 2024Link Prediction in Bipartite Networkscited by 2

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