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As a special field in deep learning, Graph Neural Networks (GNNs) focus onextracting intrinsic network features and have drawn unprecedented popularityin both academia and industry. Most of the state-of-the-art GNN models offerexpressive, robust, scalable and inductive solutions empowering social networkrecommender systems with rich network features that are computationallydifficult to leverage with graph traversal based methods.  Most recent GNNs follow an encoder-decoder paradigm to encode highdimensional heterogeneous information from a subgraph onto one low dimensionalembedding space. However, one single embedding space usually fails to captureall aspects of graph signals. In this work, we propose boosting-based metalearner for GNNs, which automatically learns multiple projections and thecorresponding embedding spaces that captures different aspects of the graphsignals. As a result, similarities between sub-graphs are quantified byembedding proximity on multiple embedding spaces. AdaGNN performs exceptionallywell for applications with rich and diverse node neighborhood information.Moreover, AdaGNN is compatible with any inductive GNNs for both node-level andedge-level tasks.

AdaGNN: A multi-modal latent representation meta-learner for GNNs based on AdaBoosting