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With the rapid development of digital platforms, users can now interact inendless ways from writing business reviews and comments to sharing informationwith their friends and followers. As a result, organizations have numerousdigital social networks available for graph learning problems with littleguidance on how to select the right graph or how to combine multiple edgetypes. In this paper, we first describe the types of user-to-user networksavailable across the Facebook (FB) and Instagram (IG) platforms. We observeminimal edge overlap between these networks, indicating users are exhibitingdifferent behaviors and interaction patterns between platforms. We then comparepredictive performance metrics across various node attribute prediction tasksfor an ads click prediction task on Facebook and for a publicly availabledataset from the Open Graph Benchmark. We adapt an existing node attributeprediction method for binary prediction, LINK-Naive Bayes, to account for bothedge direction and weights on single-layer networks. We observe meaningfulpredictive performance gains when incorporating edge direction and weight. Wethen introduce an approach called MultiLayerLINK-NaiveBayes that can combinemultiple network layers during training and observe superior performance overthe single-layer results. Ultimately, whether edge direction, edge weights, andmulti-layers are practically useful will depend on the particular setting. Ourapproach enables practitioners to quickly combine multiple layers andadditional edge information such as direction or weight.

Node Attribute Prediction on Multilayer Networks with Weighted and Directed Edges