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05/25/2023
From Latent Graph to Latent Topology Inference: Differentiable Cell Complex Module
Latent Graph Inference (LGI) relaxed the reliance of Graph Neural Networ...
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04/14/2023
Combining Stochastic Explainers and Subgraph Neural Networks can Increase Expressivity and Interpretability
Subgraph-enhanced graph neural networks (SGNN) can increase the expressi...
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02/22/2023
Drop Edges and Adapt: a Fairness Enforcing Fine-tuning for Graph Neural Networks
The rise of graph representation learning as the primary solution for ma...
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09/16/2022
Explainability in subgraphs-enhanced Graph Neural Networks
Recently, subgraphs-enhanced Graph Neural Networks (SGNNs) have been int...
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09/20/2021
A Meta-Learning Approach for Training Explainable Graph Neural Networks
In this paper, we investigate the degree of explainability of graph neur...
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04/29/2021
Biased Edge Dropout for Enhancing Fairness in Graph Representation Learning
Graph representation learning has become a ubiquitous component in many ...
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07/13/2020
Distributed Graph Convolutional Networks
The aim of this work is to develop a fully-distributed algorithmic frame...
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02/24/2020
Adaptive Propagation Graph Convolutional Network
Graph convolutional networks (GCNs) are a family of neural network model...
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06/20/2019
Efficient data augmentation using graph imputation neural networks
Recently, data augmentation in the semi-supervised regime, where unlabel...
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05/06/2019