# TopoDetect: Framework for Topological Features Detection in Graph Embeddings

arXiv: https://arxiv.org/abs/2110.04173 · HTML: https://mlsanity.com/paper/2110.04173/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 9 references that are in the map.
- First posted on arXiv 8 Oct 2021.

## Builds on (most cited first, 6 of 6)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [Representation Learning on Graphs: Methods and Applications](https://mlsanity.com/paper/1709.05584/) (2017)
- [Exploring the Semantic Content of Unsupervised Graph Embeddings: An Empirical Study](https://mlsanity.com/paper/1806.07464/) (2018)
- [Exploring the Representational Power of Graph Autoencoder](https://mlsanity.com/paper/2106.12005/) (2021)

## Abstract (arXiv)

TopoDetect is a Python package that allows the user to investigate if important topological features, such as the Degree of the nodes, their Triangle Count, or their Local Clustering Score, are preserved in the embeddings of graph representation models. Additionally, the framework enables the visualization of the embeddings according to the distribution of the topological features among the nodes. Moreover, TopoDetect enables us to study the effect of the preservation of these features by evaluating the performance of the embeddings on downstream learning tasks such as clustering and classification.