# Evaluating Node Embeddings of Complex Networks

arXiv: https://arxiv.org/abs/2102.08275 · HTML: https://mlsanity.com/paper/2102.08275/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 37 references that are in the map.
- First posted on arXiv 16 Feb 2021.

## Builds on (most cited first, 14 of 14)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [LINE: Large-scale Information Network Embedding](https://mlsanity.com/paper/1503.03578/) (2015)
- [Representation Learning on Graphs: Methods and Applications](https://mlsanity.com/paper/1709.05584/) (2017)
- [Graph Embedding Techniques, Applications, and Performance: A Survey](https://mlsanity.com/paper/1705.02801/) (2017)
- [VERSE: Versatile Graph Embeddings from Similarity Measures](https://mlsanity.com/paper/1803.04742/) (2018)
- [Artificial Benchmark for Community Detection (ABCD): Fast Random Graph Model with Community Structure](https://mlsanity.com/paper/2002.00843/) (2020)
- [Emergence of Soft Communities from Geometric Preferential Attachment](https://mlsanity.com/paper/1501.06835/) (2015)
- [Propositionalization and Embeddings: Two Sides of the Same Coin](https://mlsanity.com/paper/2006.04410/) (2020)
- [An Unsupervised Framework for Comparing Graph Embeddings](https://mlsanity.com/paper/1906.04562/) (2019)
- [Ensemble Clustering for Graphs](https://mlsanity.com/paper/1809.05578/) (2018)

## Built on by (most cited first, 4 of 4)

- [A Multi-purposed Unsupervised Framework for Comparing Embeddings of Undirected and Directed Graphs](https://mlsanity.com/paper/2112.00075/) (2021)
- [Survey of Generative Methods for Social Media Analysis](https://mlsanity.com/paper/2112.07041/) (2021)
- [Unsupervised Framework for Evaluating and Explaining Structural Node Embeddings of Graphs](https://mlsanity.com/paper/2306.10770/) (2023)
- [Rethinking Node Representation Interpretation through Relation Coherence](https://mlsanity.com/paper/2411.00653/) (2024)

## Abstract (arXiv)

Graph embedding is a transformation of nodes of a graph into a set of vectors. A~good embedding should capture the graph topology, node-to-node relationship, and other relevant information about the graph, its subgraphs, and nodes. If these objectives are achieved, an embedding is a meaningful, understandable, compressed representations of a network that can be used for other machine learning tools such as node classification, community detection, or link prediction. The main challenge is that one needs to make sure that embeddings describe the properties of the graphs well. As a result, selecting the best embedding is a challenging task and very often requires domain experts. In this paper, we do a series of extensive experiments with selected graph embedding algorithms, both on real-world networks as well as artificially generated ones. Based on those experiments we formulate two general conclusions. First, if one needs to pick one embedding algorithm before running the experiments, then node2vec is the best choice as it performed best in our tests. Having said that, there is no single winner in all tests and, additionally, most embedding algorithms have hyperparameters that should be tuned and are randomized. Therefore, our main recommendation for practitioners is, if possible, to generate several embeddings for a problem at hand and then use a general framework that provides a tool for an unsupervised graph embedding comparison. This framework (introduced recently in the literature and easily available on GitHub repository) assigns the divergence score to embeddings to help distinguish good ones from bad ones.