  Learning powerful data embeddings has become a center piece in machine
learning, especially in natural language processing and computer vision
domains. The crux of these embeddings is that they are pretrained on huge
corpus of data in a unsupervised fashion, sometimes aided with transfer
learning. However currently in the graph learning domain, embeddings learned
through existing graph neural networks (GNNs) are task dependent and thus
cannot be shared across different datasets. In this paper, we present a first
powerful and theoretically guaranteed graph neural network that is designed to
learn task-independent graph embeddings, thereafter referred to as deep
universal graph embedding (DUGNN). Our DUGNN model incorporates a novel graph
neural 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-independent
graph embeddings across diverse datasets, DUGNN also reaps the benefits of
transfer learning. Through extensive experiments and ablation studies, we show
that the proposed DUGNN model consistently outperforms both the existing
state-of-art GNN models and Graph Kernels by an increased accuracy of 3% - 8%
on graph classification benchmark datasets.
