Graph Star Net for Generalized Multi-Task Learning

Abstract

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.

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Cited by in corpus (14)

• GraphSAINT: Graph Sampling Based Inductive Learning Method
• Scalable Graph Neural Networks via Bidirectional Propagation
• Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks
• KELM: Knowledge Enhanced Pre-Trained Language Representations with Message Passing on Hierarchical Relational Graphs
• Ripple Walk Training: A Subgraph-based training framework for Large and Deep Graph Neural Network
• Explainable Natural Language Processing with Matrix Product States
• Sentiment Analysis Using Averaged Weighted Word Vector Features
• The general theory of permutation equivarant neural networks and higher order graph variational encoders
• Structure-Aware Label Smoothing for Graph Neural Networks
• Quadratic GCN for Graph Classification
• NENET: An Edge Learnable Network for Link Prediction in Scene Text
• Deep Multi-Task Augmented Feature Learning via Hierarchical Graph Neural Network
• Improving Expressivity of Graph Neural Networks
• Graph-Preserving Grid Layout: A Simple Graph Drawing Method for Graph Classification using CNNs