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Learning powerful data embeddings has become a center piece in machinelearning, especially in natural language processing and computer visiondomains. The crux of these embeddings is that they are pretrained on hugecorpus of data in a unsupervised fashion, sometimes aided with transferlearning. However currently in the graph learning domain, embeddings learnedthrough existing graph neural networks (GNNs) are task dependent and thuscannot be shared across different datasets. In this paper, we present a firstpowerful and theoretically guaranteed graph neural network that is designed tolearn task-independent graph embeddings, thereafter referred to as deepuniversal graph embedding (DUGNN). Our DUGNN model incorporates a novel graphneural network (as a universal graph encoder) and leverages rich Graph Kernels(as a multi-task graph decoder) for both unsupervised learning and(task-specific) adaptive supervised learning. By learning task-independentgraph embeddings across diverse datasets, DUGNN also reaps the benefits oftransfer learning. Through extensive experiments and ablation studies, we showthat the proposed DUGNN model consistently outperforms both the existingstate-of-art GNN models and Graph Kernels by an increased accuracy of 3% - 8%on graph classification benchmark datasets.

Learning Universal Graph Neural Network Embeddings With Aid Of Transfer Learning