# Fast link prediction for large networks using spectral embedding

arXiv: https://arxiv.org/abs/1703.09693 · HTML: https://mlsanity.com/paper/1703.09693/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2017.

## Built on by (most cited first, 3 of 3)

- [Efficient Exact Resistance Distance Computation on Small-Treewidth Graphs: a Labelling Approach](https://mlsanity.com/paper/2509.05129/) (2025)
- [A Hidden Challenge of Link Prediction: Which Pairs to Check?](https://mlsanity.com/paper/2102.07878/) (2021)
- [DynACPD Embedding Algorithm for Prediction Tasks in Dynamic Networks](https://mlsanity.com/paper/2103.07080/) (2021)

## Abstract (arXiv)

Many link prediction algorithms require the computation of a similarity metric on each vertex pair, which is quadratic in the number of vertices and infeasible for large networks. We develop a class of link prediction algorithms based on a spectral embedding and the k closest pairs algorithm that are scalable to very large networks. We compare the prediction accuracy and runtime of these methods to existing algorithms on several large link prediction tasks. Our methods achieve comparable accuracy to standard algorithms but are significantly faster.