  In this work, we present graph star net (GraphStar), a novel and unified
graph neural net architecture which utilizes message-passing relay and
attention mechanism for multiple prediction tasks - node classification, graph
classification and link prediction. GraphStar addresses many earlier challenges
facing graph neural nets and achieves non-local representation without
increasing the model depth or bearing heavy computational costs. We also
propose a new method to tackle topic-specific sentiment analysis based on node
classification and text classification as graph classification. Our work shows
that 'star nodes' can learn effective graph-data representation and improve on
current methods for the three tasks. Specifically, for graph classification and
link prediction, GraphStar outperforms the current state-of-the-art models by
2-5% on several key benchmarks.
