Paribesh Regmi, Rui Li, Kishan K C
The paper introduces a Bayesian framework to adaptively determine the optimal neighborhood scope for graph neural networks, improving performance on node classification tasks across various datasets.
Graph Neural Networks (GNNs) are a type of artificial intelligence that learn from graph-structured data, like social networks or molecular structures. A key challenge in using GNNs is deciding how much of the surrounding network (or 'neighborhood') to consider when processing each node. This paper presents a new method that uses a type of statistical model called a Bayesian framework to automatically determine the best neighborhood size for each node, rather than trying different sizes manually. This approach not only saves time but also improves the accuracy of the GNNs in classifying nodes in various types of graphs.
