读书笔记3：Representation Learning on Graphs with Jumping Knowledge Networks
这篇文章针对的点是：现在的graph上的representation learning的一种方式是neighborhood aggregation，也就是在每一个节点做卷积时，都是将其周围邻居节点的信息聚合在一起。这样做的话，有k层网络，就是将每个节点的k-hop邻近节点的信息都聚合过来了。但是这种策略并不完美，Kipf & Welling在2017年的一篇关于GCN的论文中展示了，两层的GCN表
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相关文章
- 1. 读书笔记3：Representation Learning on Graphs with Jumping Knowledge Networks
- 2. Inductive Representation Learning On Large Graphs【阅读笔记】
- 3. Inductive Representation Learning on Large Graphs阅读笔记
- 4. 【论文笔记】Representation Learning of Knowledge Graphs with Entity Descriptions
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- 8. GraphSAGE_Inductive Representation Learning on Large Graphs详细笔记
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- 10. 《Inductive representation learning on large graphs》笔记