Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

As graph neural networks (GNNs) struggle with large-scale graphs due to high computational demands, graph data distillation promises to alleviate this issue by distilling a large real graph into a smaller distilled graph while maintaining comparable prediction performance for GNNs trained on both graphs. However, we observe that GNNs trained on distilled graphs may exhibit more severe group fairness issues than GNNs trained on real graphs for vanilla and fair GNNs training. Motivated by these observations, we propose fair graph distillation (FGD), an advanced graph distillation approach to generate fair distilled graphs. The challenge lies in the deficiency of sensitive attributes for nodes in the distilled graph, making most debiasing methods (e.g., regularization and adversarial debiasing) intractable for distilled graphs. We develop a simple yet effective bias metric, named coherence, for distilled graphs. Based on the proposed coherence metric, we introduce a framework for fair graph distillation using a bi-level optimization algorithm. Extensive experiments demonstrate that the proposed algorithm can achieve better prediction performance-fairness trade-offs across various datasets and GNN architectures.

In this section, we compare the symmetric solutions found in erf [2] and ReLU networks [5] to our one-neuron solution (n =1). The main difference is that both earlier studies constrain the search space to the symmetric subspace whereas we first prove that the non-trivial critical points are contained in this subspace in Theorem 5.1 for a broad class of activation functions, including erf and ReLU. Solving the low-dimensional loss, we recover the same solution for ReLU and erf as in [2, 5] for unit-orthonormal teachers.