讀書筆記3：Representation Learning on Graphs with Jumping Knowledge Networks
這篇文章針對的點是：如今的graph上的representation learning的一種方式是neighborhood aggregation，也就是在每個節點作卷積時，都是將其周圍鄰居節點的信息聚合在一塊兒。這樣作的話，有k層網絡，就是將每一個節點的k-hop鄰近節點的信息都聚合過來了。可是這種策略並不完美，Kipf & Welling在2017年的一篇關於GCN的論文中展現了，兩層的GCN
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相關文章
- 1. 讀書筆記3：Representation Learning on Graphs with Jumping Knowledge Networks
- 2. Inductive Representation Learning On Large Graphs【閱讀筆記】
- 3. Inductive Representation Learning on Large Graphs閱讀筆記
- 4. 【論文筆記】Representation Learning of Knowledge Graphs with Entity Descriptions
- 5. 論文筆記：Representation Learning of Knowledge Graphs with Entity Descriptions
- 6. 論文分享 |Jumping Knowledge Networks
- 7. Inductive Representation Learning on Large Graphs 論文閱讀
- 8. GraphSAGE_Inductive Representation Learning on Large Graphs詳細筆記
- 9. 論文筆記: Inductive Representation Learning on Large Graphs
- 10. 《Inductive representation learning on large graphs》筆記