Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks

时间 2020-12-23

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这是一篇将GNN运用在预测知识图谱(Knowledge Graph)节点重要性的文章，被KDD2019接收。文中提出了GENI模型，在GNN聚合信息的过程中只聚合一个标量（score）而不是聚合节点的embedding。 Introduction 知识图谱可以看做是一个有向多关系图，并且节点之间可能存在不止一条边。 Given a KG, estimating the importance of

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1. 论文阅读 Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks
2. Applications of Graph Neural Networks
3. Emotion Recognition Using Graph Convolutional Networks
4. EMNLP2019 | Knowledge-Aware Graph Networks
5. Learning Convolutional Neural Networks for Graphs
6. Few-Shot Learning with Graph Neural Networks
7. Structural Image Classification with Graph Neural Networks
8. CS224W-08-Graph Neural Networks
9. Paper Notes: A Comprehensive Survey on Graph Neural Networks
10. STRATEGIES FOR PRE-TRAINING GRAPH NEURAL NETWORKS
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1. 论文阅读 Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks
2. Applications of Graph Neural Networks
3. Emotion Recognition Using Graph Convolutional Networks
4. EMNLP2019 | Knowledge-Aware Graph Networks
5. Learning Convolutional Neural Networks for Graphs
6. Few-Shot Learning with Graph Neural Networks
7. Structural Image Classification with Graph Neural Networks
8. CS224W-08-Graph Neural Networks
9. Paper Notes: A Comprehensive Survey on Graph Neural Networks
10. STRATEGIES FOR PRE-TRAINING GRAPH NEURAL NETWORKS

>>更多相关文章<<