论文阅读 Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks
论文阅读 Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks 摘要 如何评估知识图（KG）中节点的重要性？KG是一个多关系图，已被证明在许多任务上都有价值，包括问答和语义搜索。本文提出了一种解决KGs中节点重要性评估问题的GENI方法，该方法可以用于项目推荐和资源分配等下游应用。虽然已经有了一些方法来
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相关文章
- 1. Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks
- 2. Applications of Graph Neural Networks
- 3. Learning Convolutional Neural Networks for Graphs论文笔记
- 4. 论文解读：Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base Embeddings
- 5. Inductive Representation Learning on Large Graphs 论文阅读
- 6. 论文阅读：Extending Neural Generative Conversational Model using External Knowledge Sources
- 7. 论文阅读：Learning Human-Object Interactions by Graph Parsing Neural Networks
- 8. 【论文解读 ICLR 2020 | LambdaNet】Probabilistic Type Inference using Graph Neural Networks
- 9. 论文阅读：Knowledge Graph Convolutional Networks for Recommender Systems
- 10. Emotion Recognition Using Graph Convolutional Networks