TopoDetect: Framework for Topological Features Detection in Graph Embeddings

10/08/2021
by   Maroun Haddad, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/22/2021

Exploring the Representational Power of Graph Autoencoder

While representation learning has yielded a great success on many graph ...
research
07/16/2019

DeepTrax: Embedding Graphs of Financial Transactions

Financial transactions can be considered edges in a heterogeneous graph ...
research
06/19/2018

Exploring the Semantic Content of Unsupervised Graph Embeddings: An Empirical Study

Graph embeddings have become a key and widely used technique within the ...
research
02/17/2021

DeepWalking Backwards: From Embeddings Back to Graphs

Low-dimensional node embeddings play a key role in analyzing graph datas...
research
10/18/2021

Topologically Regularized Data Embeddings

Unsupervised feature learning often finds low-dimensional embeddings tha...
research
02/16/2020

Topological Mapping for Manhattan-like Repetitive Environments

We showcase a topological mapping framework for a challenging indoor war...
research
06/01/2017

Statistical Analysis and Parameter Selection for Mapper

In this article, we study the question of the statistical convergence of...

Please sign up or login with your details

Forgot password? Click here to reset