Graph Neural Networks (GNNs) have shown satisfying performance on various graph learning tasks. To achieve better fitting capability, most GNNs are with a large number of parameters, which makes these GNNs computationally expensive. Therefore, it is difficult to deploy them onto edge devices with scarce computational resources, e.g., mobile phones and wearable smart devices. Knowledge Distillation (KD) is a common solution to compress GNNs, where a light-weighted model (i.e., the student model) is encouraged to mimic the behavior of a computationally expensive GNN (i.e., the teacher GNN model). Nevertheless, most existing GNN-based KD methods lack fairness consideration. As a consequence, the student model usually inherits and even exaggerates the bias from the teacher GNN. To handle such a problem, we take initial steps towards fair knowledge distillation for GNNs. Specifically, we first formulate a novel problem of fair knowledge distillation for GNN-based teacher-student frameworks. Then we propose a principled framework named RELIANT to mitigate the bias exhibited by the student model. Notably the design of RELIANT is decoupled from any specific teacher and student model structures, and thus can be easily adapted to various GNN-based KD frameworks. We perform extensive experiments on multiple real-world datasets, which corroborates that RELIANT achieves less biased GNN knowledge distillation while maintaining high prediction utility. Open-source code can be found at https://github.com/yushundong/RELIANT.
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Bayesian Graph Neural Networks with Adaptive Connection Sampling
We propose a unified framework for adap- tive connection sampling in graph neural net- works (GNNs) that generalizes existing stochas- tic regularization methods for training GNNs. The proposed framework not only alleviates over- smoothing and over-fitting tendencies of deep GNNs, but also enables learning with uncertainty in graph analytic tasks with GNNs. Instead of using fixed sampling rates or hand-tuning them as model hyperparameters as in existing stochas- tic regularization methods, our adaptive connec- tion sampling can be trained jointly with GNN model parameters in both global and local fash- ions. GNN training with adaptive connection sampling is shown to be mathematically equiv- alent to an efficient approximation of training Bayesian GNNs. Experimental results with abla- tion studies on benchmark datasets validate that adaptively learning the sampling rate given graph training data is the key to boosting the perfor- mance of GNNs in semi-supervised node classifi- cation, making them less prone to over-smoothing and over-fitting with more robust prediction.
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- NSF-PAR ID:
- 10209364
- Date Published:
- Journal Name:
- International Conference on Machine Learning
- Volume:
- 37
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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