Reference code and data for analyzing the tradeoff between accuracy and algorithmic fairness on graph neural networks for node classification. This repository provides support for downloading and preprocessing four datasets (German, Credit, Penn94, and Pokec-Z), building and training three GNN models (GCN, GraphSAGE, and GIN), and implementing algorithmic fairness interventions including PFR-AX and PostProcess (ours), Unaware, EDITS, and NIFTY (baselines)
© 2019, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserv...
Recent approaches to behavioural user profiling employ Graph Neural Networks (GNNs) to turn users' i...
As the representations output by Graph Neural Networks (GNNs) are increasingly employed in real-worl...
Graph Neural Networks (GNNs) have become increasingly important due to their representational power ...
Graph Neural Networks (GNNs) have emerged as the leading paradigm for solving graph analytical probl...
Graph Neural Networks (GNNs) have shown great power in learning node representations on graphs. Howe...
Graph generation models have gained increasing popularity and success across various domains. Howeve...
Graph neural networks (GNNs), has been widely used for supervised learning tasks in graphs reaching ...
Recently, there is growing concern that machine-learned software, which currently assists or even au...
Recently, there is growing concern that machine-learning models, which currently assist or even auto...
25 pagesNowadays, the analysis of complex phenomena modeled by graphs plays a crucial role in many r...
We consider the problem of whether a Neural Network (NN) model satisfies global individual fairness....
With Deep Neural Network (DNN) being integrated into a growing number of critical systems with far-r...
There is currently a great expansion of the impact of machine learning algorithms on our lives, prom...
Graph Neural Networks (GNNs) have improved unsupervised community detection of clustered nodes due t...
© 2019, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserv...
Recent approaches to behavioural user profiling employ Graph Neural Networks (GNNs) to turn users' i...
As the representations output by Graph Neural Networks (GNNs) are increasingly employed in real-worl...
Graph Neural Networks (GNNs) have become increasingly important due to their representational power ...
Graph Neural Networks (GNNs) have emerged as the leading paradigm for solving graph analytical probl...
Graph Neural Networks (GNNs) have shown great power in learning node representations on graphs. Howe...
Graph generation models have gained increasing popularity and success across various domains. Howeve...
Graph neural networks (GNNs), has been widely used for supervised learning tasks in graphs reaching ...
Recently, there is growing concern that machine-learned software, which currently assists or even au...
Recently, there is growing concern that machine-learning models, which currently assist or even auto...
25 pagesNowadays, the analysis of complex phenomena modeled by graphs plays a crucial role in many r...
We consider the problem of whether a Neural Network (NN) model satisfies global individual fairness....
With Deep Neural Network (DNN) being integrated into a growing number of critical systems with far-r...
There is currently a great expansion of the impact of machine learning algorithms on our lives, prom...
Graph Neural Networks (GNNs) have improved unsupervised community detection of clustered nodes due t...
© 2019, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserv...
Recent approaches to behavioural user profiling employ Graph Neural Networks (GNNs) to turn users' i...
As the representations output by Graph Neural Networks (GNNs) are increasingly employed in real-worl...
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