Process / pipeline

Link Prediction — Missing and Future Edge Inference in Networks

Link prediction is a network-analysis task that estimates which edges are missing from an observed graph or which edges are likely to form in the future. Formalised by Liben-Nowell and Kleinberg (2003, 2007), it covers a spectrum of approaches — from simple structural similarity indices such as Common Neighbors, Jaccard coefficient, and Adamic-Adar, to matrix factorisation, and graph neural network (GNN) methods — and is evaluated with AUC and Average Precision to account for the heavily imbalanced ratio of real to non-existing edges.

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Sources

  1. Liben-Nowell, D. & Kleinberg, J. (2007). The Link-Prediction Problem for Social Networks. Journal of the American Society for Information Science and Technology, 58(7), 1019-1031. DOI: 10.1002/asi.20591
  2. Zhang, M. & Chen, Y. (2018). Link Prediction Based on Graph Neural Networks. Advances in Neural Information Processing Systems (NeurIPS), 31. link

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Referenced by

ScholarGateLink Prediction (Link Prediction (Missing and Future Edge Inference)). Retrieved 2026-06-04 from https://scholargate.app/en/network-analysis/link-prediction