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Found 9 papers, 4 papers with code
Addressing Shortcomings in Fair Graph Learning Datasets: Towards a New Benchmark
These datasets are thoughtfully designed to include relevant graph structures and bias information crucial for the fair evaluation of models.
On the Safety of Open-Sourced Large Language Models: Does Alignment Really Prevent Them From Being Misused?
A natural question is "could alignment really prevent those open-sourced large language models from being misused to generate undesired content?''.
Towards Fair Graph Neural Networks via Graph Counterfactual
Despite their great performance in modeling graphs, recent works show that GNNs tend to inherit and amplify the bias from training data, causing concerns of the adoption of GNNs in high-stake scenarios.
Fairness-aware Message Passing for Graph Neural Networks
Graph Neural Networks (GNNs) have shown great power in various domains.
Counterfactual Learning on Graphs: A Survey
To facilitate the development of this promising direction, in this survey, we categorize and comprehensively review papers on graph counterfactual learning.
Link Prediction on Heterophilic Graphs via Disentangled Representation Learning
Therefore, in this paper, we study a novel problem of exploring disentangled representation learning for link prediction on heterophilic graphs.
Decoupled Self-supervised Learning for Non-Homophilous Graphs
This paper studies the problem of conducting self-supervised learning for node representation learning on graphs.
A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability
Despite their great potential in benefiting humans in the real world, recent study shows that GNNs can leak private information, are vulnerable to adversarial attacks, can inherit and magnify societal bias from training data and lack interpretability, which have risk of causing unintentional harm to the users and society.
Label-Wise Graph Convolutional Network for Heterophilic Graphs
Graph Neural Networks (GNNs) have achieved remarkable performance in modeling graphs for various applications.
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