論文閱讀筆記：Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks
這篇論文好像是第一篇將圖神經網絡應用到社交媒體謠言檢測的論文。 摘要 摘要中，有幾個關鍵性的詞，有助於我們從全局瞭解這篇paper的內容，以下： Bi-Directional：意思是「雙向的」，分別指「top-down」，「down-top」； top-down：指的是謠言自頂向下傳播（propagation）； 「down-top」：指的是謠言 模型 採用了two-layer 1stChebNe
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相關文章
- 1. 【論文解讀 AAAI 2020 | Bi-GCN】Rumor Detection on Social Media with Bi-Directional GCN
- 2. 論文閱讀筆記：GAS---spam review detection with graph convolutional networks
- 3. Multi-Label Image Recognition with Graph Convolutional Networks 論文閱讀筆記
- 4. 論文閱讀筆記: Modeling Relational Data with Graph Convolutional Networks
- 5. 【論文閱讀筆記】Modeling Sentiment Dependencies with Graph Convolutional Networks for Aspect-level Sentiment
- 6. 論文閱讀：Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks
- 7. 【論文解讀 arXiv 2020 | RA-GCN】Event Detection with Relation-Aware Graph Convolutional Networks
- 8. GRAPH ATTENTION NETWORKS--論文閱讀筆記
- 9. Graph Convolutional Networks for Text Classification閱讀筆記
- 10. 《Simple and Deep Graph Convolutional Networks》--論文閱讀筆記