Node Attribute Prediction with Weighted and Directed Edges on Single and Multilayer Networks

Abstract

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. For example, while user-to-user interactions are directed in nature, many graph learning approaches use the undirected version of the network. In this paper, we introduce edge direction, edge weight, and multi-relational data for node prediction tasks. We first 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 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 (OGB). We observe meaningful predictive performance improvements when incorporating edge direction and weight, and performance that's competitive with the OGB Leaderboard. 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 edge types.