Heterogeneous Graph Attention Network

@article{Wang2019HeterogeneousGA,
  title={Heterogeneous Graph Attention Network},
  author={X. Wang and Houye Ji and C. Shi and Bai Wang and Peng Cui and P. Yu and Yanfang Ye},
  journal={The World Wide Web Conference},
  year={2019}
}
Graph neural network, as a powerful graph representation technique based on deep learning, has shown superior performance and attracted considerable research interest. [...] Key Method With the learned importance from both node-level and semantic-level attention, the importance of node and meta-path can be fully considered. Then the proposed model can generate node embedding by aggregating features from meta-path based neighbors in a hierarchical manner. Extensive experimental results on three real-world…Expand
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References

SHOWING 1-3 OF 3 REFERENCES
Graph Attention Networks
  • 2,873
  • Highly Influential
  • PDF
Heterogeneous Information Network Embedding for Recommendation
  • 267
  • Highly Influential
  • PDF
Semi-Supervised Classification with Graph Convolutional Networks
  • 6,754
  • Highly Influential
  • PDF