# A Multi-purposed Unsupervised Framework for Comparing Embeddings of Undirected and Directed Graphs

arXiv: https://arxiv.org/abs/2112.00075 · HTML: https://mlsanity.com/paper/2112.00075/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 40 references that are in the map.
- First posted on arXiv 30 Nov 2021.

## Builds on (most cited first, 12 of 12)

- [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)
- [Motifs in Temporal Networks](https://mlsanity.com/paper/1612.09259/) (2016)
- [Artificial Benchmark for Community Detection (ABCD): Fast Random Graph Model with Community Structure](https://mlsanity.com/paper/2002.00843/) (2020)
- [Node Representation Learning for Directed Graphs](https://mlsanity.com/paper/1810.09176/) (2018)
- [Progresses and Challenges in Link Prediction](https://mlsanity.com/paper/2102.11472/) (2021)
- [Community detection in networks using graph embeddings](https://mlsanity.com/paper/2009.05265/) (2020)
- [Emergence of Soft Communities from Geometric Preferential Attachment](https://mlsanity.com/paper/1501.06835/) (2015)
- [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)
- [Evaluating Node Embeddings of Complex Networks](https://mlsanity.com/paper/2102.08275/) (2021)
- [From Symbols to Embeddings: A Tale of Two Representations in Computational Social Science](https://mlsanity.com/paper/2106.14198/) (2021)

## Built on by (most cited first, 6 of 6)

- [Artificial Benchmark for Community Detection with Outliers (ABCD+o)](https://mlsanity.com/paper/2301.05749/) (2023)
- [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)
- [Predicting Properties of Nodes via Community-Aware Features](https://mlsanity.com/paper/2311.04730/) (2023)
- [Network Embedding Exploration Tool (NEExT)](https://mlsanity.com/paper/2503.15853/) (2025)
- [Leveraging Non-linear Dimension Reduction and Random Walk Co-occurrence for Node Embedding](https://mlsanity.com/paper/2602.24069/) (2026)

## Abstract (arXiv)

Graph embedding is a transformation of nodes of a network into a set of vectors. A good embedding should capture the underlying graph topology and structure, node-to-node relationship, and other relevant information about the graph, its subgraphs, and nodes themselves. If these objectives are achieved, an embedding is a meaningful, understandable, and often compressed representation of a network. Unfortunately, selecting the best embedding is a challenging task and very often requires domain experts. In this paper, we extend the framework for evaluating graph embeddings that was recently introduced by the authors. Now, the framework assigns two scores, local and global, to each embedding that measure the quality of an evaluated embedding for tasks that require good representation of local and, respectively, global properties of the network. The best embedding, if needed, can be selected in an unsupervised way, or the framework can identify a few embeddings that are worth further investigation. The framework is flexible, scalable, and can deal with undirected/directed, weighted/unweighted graphs.