# Graph Star Net for Generalized Multi-Task Learning

arXiv: https://arxiv.org/abs/1906.12330 · HTML: https://mlsanity.com/paper/1906.12330/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2019.

## Built on by (most cited first, 11 of 11)

- [KELM: Knowledge Enhanced Pre-Trained Language Representations with Message Passing on Hierarchical Relational Graphs](https://mlsanity.com/paper/2109.04223/) (2021)
- [Bag-of-Words vs. Graph vs. Sequence in Text Classification: Questioning the Necessity of Text-Graphs and the Surprising Strength of a Wide MLP](https://mlsanity.com/paper/2109.03777/) (2021)
- [Constructing Flow Graphs from Procedural Cybersecurity Texts](https://mlsanity.com/paper/2105.14357/) (2021)
- [Explainable Natural Language Processing with Matrix Product States](https://mlsanity.com/paper/2112.08628/) (2021)
- [Graph Representation Learning for Multi-Task Settings: a Meta-Learning Approach](https://mlsanity.com/paper/2201.03326/) (2022)
- [Structure-Aware Label Smoothing for Graph Neural Networks](https://mlsanity.com/paper/2112.00499/) (2021)
- [Measuring and Sampling: A Metric-guided Subgraph Learning Framework for Graph Neural Network](https://mlsanity.com/paper/2112.15015/) (2021)
- [CNN-Trans-Enc: A CNN-Enhanced Transformer-Encoder On Top Of Static BERT representations for Document Classification](https://mlsanity.com/paper/2209.06344/) (2022)
- [Global Hierarchical Neural Networks using Hierarchical Softmax](https://mlsanity.com/paper/2308.01210/) (2023)
- [Quadratic GCN for Graph Classification](https://mlsanity.com/paper/2104.06750/) (2021)
- [Graph Decipher: A transparent dual-attention graph neural network to understand the message-passing mechanism for the node classification](https://mlsanity.com/paper/2201.01381/) (2022)

## Abstract (arXiv)

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.