  With the rapid development of digital platforms, users can now interact in
endless ways from writing business reviews and comments to sharing information
with their friends and followers. As a result, organizations have numerous
digital social networks available for graph learning problems with little
guidance on how to select the right graph or how to combine multiple edge
types. In this paper, we first describe the types of user-to-user networks
available across the Facebook (FB) and Instagram (IG) platforms. We observe
minimal edge overlap between these networks, indicating users are exhibiting
different behaviors and interaction patterns between platforms. We then compare
predictive performance metrics across various node attribute prediction tasks
for an ads click prediction task on Facebook and for a publicly available
dataset from the Open Graph Benchmark. We adapt an existing node attribute
prediction method for binary prediction, LINK-Naive Bayes, to account for both
edge direction and weights on single-layer networks. We observe meaningful
predictive performance gains when incorporating edge direction and weight. We
then introduce an approach called MultiLayerLINK-NaiveBayes that can combine
multiple network layers during training and observe superior performance over
the single-layer results. Ultimately, whether edge direction, edge weights, and
multi-layers are practically useful will depend on the particular setting. Our
approach enables practitioners to quickly combine multiple layers and
additional edge information such as direction or weight.
