  A biological system is a complex network of heterogeneous molecular entities
and their interactions contributing to various biological characteristics of
the system. However, current biological networks are noisy, sparse, and
incomplete, limiting our ability to create a holistic view of the biological
system and understand the biological phenomena. Experimental identification of
such interactions is both time-consuming and expensive. With the recent
advancements in high-throughput data generation and significant improvement in
computational power, various computational methods have been developed to
predict novel interactions in the noisy network. Recently, deep learning
methods such as graph neural networks have shown their effectiveness in
modeling graph-structured data and achieved good performance in biomedical
interaction prediction. However, graph neural networks-based methods require
human expertise and experimentation to design the appropriate complexity of the
model and significantly impact the performance of the model. Furthermore, deep
graph neural networks face overfitting problems and tend to be poorly
calibrated with high confidence on incorrect predictions. To address these
challenges, we propose Bayesian model selection for graph convolutional
networks to jointly infer the most plausible number of graph convolution layers
(depth) warranted by data and perform dropout regularization simultaneously.
Experiments on four interaction datasets show that our proposed method achieves
accurate and calibrated predictions. Our proposed method enables the graph
convolutional networks to dynamically adapt their depths to accommodate an
increasing number of interactions.
