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Unsupervised and Inherently Interpretable Graph Embeddings

Unsupervised and Inherently Interpretable Graph Embeddings

Unsupervised and Inherently Interpretable Graph Embeddings - arXiv

Title:Augment to Interpret: Unsupervised and Inherently Interpretable Graph Embeddings ... Abstract:Unsupervised learning allows us to leverage ...

Unsupervised and Inherently Interpretable Graph Embeddings

We propose INGENIOUS (INherently INterpretable Graph and Node Unsupervised embeddings), a framework that generalises over existing approaches based on learned.

Unsupervised and Inherently Interpretable Graph Embeddings

This paper studies graph representation learning and shows that data augmentation that preserves semantics can be learned and used to produce ...

Unsupervised and Inherently Interpretable Graph Embeddings

Augment to Interpret: Unsupervised and Inherently Interpretable Graph Embeddings ... In this paper, we study graph representation learning, and we ...

Augment to Interpret: Unsupervised and Inherently Interpretable ...

Augment to Interpret: Unsupervised and Inherently Interpretable Graph Embeddings. Gregory Scafarto, Madalina Ciortan, Simon Tihon, Quentin Ferre.

Unsupervised and Inherently Interpretable Graph Embeddings

Augment to Interpret: Unsupervised and Inherently Interpretable Graph Embeddings. Gregory Scafarto, Madalina Ciortan, Simon Tihon, Quentin Ferre. Sep 28 2023.

gregory scafarto on LinkedIn: Augment to Interpret

... GraphRepresentation #Interpretability #ACML2023 · Augment to ...

euranova/Augment_to_Interpret: ACML paper repository - GitHub

... Interpret: Unsupervised and Inherently Interpretable Graph Embeddings, by G. Scafarto, M. Ciortan, S. Tihon and Q. Ferré. How to install. This repository ...

Madalina Ciortan on LinkedIn: Augment to Interpret: Unsupervised ...

Madalina Ciortan's Post · Augment to Interpret: Unsupervised and Inherently Interpretable Graph Embeddings · More Relevant Posts · Human Feedback is not Gold ...

Augment to Interpret: Unsupervised and Inherently Interpretable ...

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DINE: Dimensional Interpretability of Node Embeddings - IEEE Xplore

Graph representation learning methods, such as node embeddings, are powerful approaches to map nodes into a latent vector space, ...

‪Simon Tihon‬ - ‪Google Scholar‬

Augment to Interpret: Unsupervised and Inherently Interpretable Graph Embeddings. G Scafarto, M Ciortan, S Tihon, Q Ferre. Asian Conference on Machine Learning, ...

Unsupervised Graph Representation Learning with Cluster-aware ...

First, we propose a novel unsupervised GNN model with cluster-aware self-training, which learns embeddings using intrinsic network cluster ...

Towards Explanation for Unsupervised Graph-Level Representation ...

Augment to Interpret: Unsupervised and Inherently Interpretable Graph Embeddings · Gregory ScafartoMadalina CiortanSimon TihonQuentin Ferre. Computer Science.

Pre-training Interpretable Graph Neural Networks - NIPS papers

Intrinsic interpretable graph neural networks aim to provide transparent predictions ... models aim to provide good graph embeddings for various tasks, while the ...

Madalina Ciortan - Papers With Code

Augment to Interpret: Unsupervised and Inherently Interpretable Graph Embeddings · 1 code implementation • 28 Sep 2023 • Gregory Scafarto, Madalina Ciortan, ...

Unsupervised Graph Embedding via Adaptive Graph Learning ...

Augment to Interpret: Unsupervised and Inherently Interpretable Graph Embeddings. Gregory Scafarto, Madalina Ciortan, Simon Tihon, ...

Graph Representation Learning - an overview | ScienceDirect Topics

In addition, the produced Gaussian embeddings are interpretable as the uncertainty of the embedding ... The methods based on two-stage training use unsupervised ...

Exploring the Semantic Content of Unsupervised Graph Embeddings

... inherently complex structures and do not natu-. rally lend ... an increased level of interpretability to graph embeddings. Inspired ...

Interpretability in Graph Neural Networks

First, GNN still maps nodes and links into embeddings. Therefore, similar to traditional deep models, GNN also suffers from the opacity of information ...