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Found 8 papers, 5 papers with code
Revisiting and Benchmarking Graph Autoencoders: A Contrastive Learning Perspective
Graph autoencoders (GAEs) are self-supervised learning models that can learn meaningful representations of graph-structured data by reconstructing the input graph from a low-dimensional latent space.
One Fits All: Learning Fair Graph Neural Networks for Various Sensitive Attributes
Accordingly, we propose a graph fairness framework based on invariant learning, namely FairINV, which enables the training of fair GNNs to accommodate various sensitive attributes within a single training session.
Fair Graph Representation Learning via Sensitive Attribute Disentanglement
In particular, the objective of group fairness is to ensure that the decisions made by GNNs are independent of the sensitive attribute.
The Devil is in the Data: Learning Fair Graph Neural Networks via Partial Knowledge Distillation
Experiments on several benchmark datasets demonstrate that FairGKD, which does not require access to demographic information, significantly improves the fairness of GNNs by a large margin while maintaining their utility.
LasTGL: An Industrial Framework for Large-Scale Temporal Graph Learning
Over the past few years, graph neural networks (GNNs) have become powerful and practical tools for learning on (static) graph-structure data.
Hetero$^2$Net: Heterophily-aware Representation Learning on Heterogenerous Graphs
Through in-depth investigations on several real-world heterogeneous graphs exhibiting varying levels of heterophily, we have observed that heterogeneous graph neural networks (HGNNs), which inherit many mechanisms from GNNs designed for homogeneous graphs, fail to generalize to heterogeneous graphs with heterophily or low level of homophily.
Oversmoothing: A Nightmare for Graph Contrastive Learning?
Oversmoothing is a common phenomenon observed in graph neural networks (GNNs), in which an increase in the network depth leads to a deterioration in their performance.
AutoMine: An Unmanned Mine Dataset
The main contributions of the AutoMine dataset are as follows: 1. The first autonomous driving dataset for perception and localization in mine scenarios.
