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.