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Multigprompt for multi-task pre-training and prompting on graphs
Graph Neural Networks (GNNs) have emerged as a mainstream technique for graph
representation learning. However, their efficacy within an end-to-end supervised framework …
representation learning. However, their efficacy within an end-to-end supervised framework …
Generalized graph prompt: Toward a unification of pre-training and downstream tasks on graphs
X Yu, Z Liu, Y Fang, Z Liu, S Chen… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Graphs can model complex relationships between objects, enabling a myriad of Web
applications such as online page/article classification and social recommendation. While …
applications such as online page/article classification and social recommendation. While …
Supervised algorithmic fairness in distribution shifts: A survey
M Shao, D Li, C Zhao, X Wu, Y Lin, Q Tian - arxiv preprint arxiv …, 2024 - arxiv.org
Supervised fairness-aware machine learning under distribution shifts is an emerging field
that addresses the challenge of maintaining equitable and unbiased predictions when faced …
that addresses the challenge of maintaining equitable and unbiased predictions when faced …
Is it still fair? A comparative evaluation of fairness algorithms through the lens of covariate drift
Over the last few decades, machine learning (ML) applications have grown exponentially,
yielding several benefits to society. However, these benefits are tempered with concerns of …
yielding several benefits to society. However, these benefits are tempered with concerns of …
Bridging the Fairness Divide: Achieving Group and Individual Fairness in Graph Neural Networks
Graph neural networks (GNNs) have emerged as a powerful tool for analyzing and learning
from complex data structured as graphs, demonstrating remarkable effectiveness in various …
from complex data structured as graphs, demonstrating remarkable effectiveness in various …
Disentangling, Amplifying, and Debiasing: Learning Disentangled Representations for Fair Graph Neural Networks
YC Lee, H Shin, SW Kim - arxiv preprint arxiv:2408.12875, 2024 - arxiv.org
Graph Neural Networks (GNNs) have become essential tools for graph representation
learning in various domains, such as social media and healthcare. However, they often …
learning in various domains, such as social media and healthcare. However, they often …
Improving Graph Representation Learning with Augmentations, Uncertainty Quantification and Large Language Models
P Trivedi - 2024 - deepblue.lib.umich.edu
Expressive graph representation learning is important to many high-impact applications as
structured data across many domains can be naturally represented using graphs. While the …
structured data across many domains can be naturally represented using graphs. While the …