TopoDetect: Framework for Topological Features Detection in Graph Embeddings

TL;DR

TopoDetect is a Python framework that assesses and visualizes the preservation of topological features in graph embeddings and evaluates their impact on downstream tasks.

Contribution

It introduces a comprehensive tool for analyzing topological feature preservation in graph embeddings and their influence on learning performance.

Findings

Effective visualization of topological feature distributions.

Quantitative assessment of feature preservation impact.

Facilitates understanding of embedding quality in graph analysis.

Abstract

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.