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Title: 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.  more » « less
Award ID(s):
1934904 1839816 1848596
NSF-PAR ID:
10209364
Author(s) / Creator(s):
; ; ; ; ; ;
Date Published:
Journal Name:
International Conference on Machine Learning
Volume:
37
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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