# EvalNE: A Framework for Evaluating Network Embeddings on Link Prediction

arXiv: https://arxiv.org/abs/1901.09691 · HTML: https://mlsanity.com/paper/1901.09691/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2019.

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

- [Fund2Vec: Mutual Funds Similarity using Graph Learning](https://mlsanity.com/paper/2106.12987/) (2021)
- [Exploring the Performance of Continuous-Time Dynamic Link Prediction Algorithms](https://mlsanity.com/paper/2405.17182/) (2024)
- [MyDigitalFootprint: an extensive context dataset for pervasive computing applications at the edge](https://mlsanity.com/paper/2306.15990/) (2023)

## Abstract (arXiv)

In this paper we present EvalNE, a Python toolbox for evaluating network embedding methods on link prediction tasks. Link prediction is one of the most popular choices for evaluating the quality of network embeddings. However, the complexity of this task requires a carefully designed evaluation pipeline in order to provide consistent, reproducible and comparable results. EvalNE simplifies this process by providing automation and abstraction of tasks such as hyper-parameter tuning and model validation, edge sampling and negative edge sampling, computation of edge embeddings from node embeddings, and evaluation metrics. The toolbox allows for the evaluation of any off-the-shelf embedding method without the need to write extra code. Moreover, it can also be used for evaluating any other link prediction method, and integrates several link prediction heuristics as baselines.