# Meta Propagation Networks for Graph Few-shot Semi-supervised Learning

@inproceedings{Ding2022MetaPN, title={Meta Propagation Networks for Graph Few-shot Semi-supervised Learning}, author={Kaize Ding and Jianling Wang and James Caverlee and Huan Liu}, booktitle={AAAI}, year={2022} }

Inspired by the extensive success of deep learning, graph neural networks (GNNs) have been proposed to learn expressive node representations and demonstrated promising performance in various graph learning tasks. However, existing endeavors predominately focus on the conventional semi-supervised setting where relatively abundant gold-labeled nodes are provided. While it is often impractical due to the fact that data labeling is unbearably laborious and requires intensive domain knowledge…

## 3 Citations

Few-Shot Learning on Graphs

- Computer ScienceProceedings of the Thirty-First International Joint Conference on Artificial Intelligence
- 2022

This paper comprehensively surveyes existing work of FSLG in terms of three major graph mining tasks at different granularity levels, i.e., node, edge, and graph.

Data Augmentation for Deep Graph Learning: A Survey

- Computer ScienceArXiv
- 2022

A taxonomy for graph data augmentation is proposed and a structured review by categorizing the related work based on the augmented information modalities is provided, focusing on the two challenging problems in DGL (i.e., optimal graph learning and low-resource graph learning).

Structural and Semantic Contrastive Learning for Self-supervised Node Representation Learning

- Computer ScienceArXiv
- 2022

This work proposes a simple yet effective framework – Simple Neural Networks with Structural and Semantic Contrastive Learning (S 3 -CL), which proves that even a simple neural network is able to learn expressive node representations that preserve valuable global structural and semantic patterns.

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