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Tutorial: Graph Rewiring: From Theory to Applications in Fairness
Understanding Over-Squashing and Bottlenecks on Graphs via Curvature | Jake Topping & F. Di Giovanni
FairRankVis: A Visual Analytics Framework for Exploring Algorithmic Fairness in Graph Mining Models
Learning Fairness and Graph Deep Generation in Dynamic Environments
FairMatch: A Graph-based Approach for Improving Aggregate Diversity in Recommender Systems
Efficient Graph Neural Networks: How can graphs go from last to fast? | MLSys 2021
Revisiting Group Fairness Metrics: The Effect of Networks
NeurIPS 2021 || DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks Paper
Interpretability and Algorithmic Fairness
Provable Advantages for Graph Algorithms in Spiking Neural Networks
Lecture 16 Structure Learning
Dynamics and Generalization in deep neural networks
