【KDD 2019】Is a Single Vector Enough Exploring Node Polysemy for Network Embedding
摘要 Networks have been widely used as the data structure for abstracting real-world systems as well as organizing the relations among entities. Network embedding models are powerful tools in mapping no
相關文章
- 1. 【KDD 2019】Heterogeneous Graph Neural Network
- 2. EmbeddingVis: A Visual Analytics Approach to Comparative Network Embedding Inspection
- 3. 【KDD 2019】A Representation Learning Framework for Property Graphs
- 4. A Survey of Network Embedding
- 5. 【論文解讀 KDD 2019 | GATNE 】Representation Learning for Attributed Multiplex Heterogeneous Network
- 6. 【論文解讀 KDD 2019 | MEIRec】Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation
- 7. Exploring the Network
- 8. A Cascaded Convolutional Neural Network for Single Image Dehazing
- 9. SRMD:Learning a Single Convolutional Super-Resolution Network for Multiple Degradations
- 10. network embedding
- 更多相關文章...
- • XML DOM - Node 對象 - XML DOM 教程
- • Scala for循環 - Scala教程
- • 算法總結-廣度優先算法
- • Kotlin學習（一）基本語法
相關標籤/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
歡迎關注本站公眾號,獲取更多信息
相關文章
- 1. 【KDD 2019】Heterogeneous Graph Neural Network
- 2. EmbeddingVis: A Visual Analytics Approach to Comparative Network Embedding Inspection
- 3. 【KDD 2019】A Representation Learning Framework for Property Graphs
- 4. A Survey of Network Embedding
- 5. 【論文解讀 KDD 2019 | GATNE 】Representation Learning for Attributed Multiplex Heterogeneous Network
- 6. 【論文解讀 KDD 2019 | MEIRec】Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation
- 7. Exploring the Network
- 8. A Cascaded Convolutional Neural Network for Single Image Dehazing
- 9. SRMD:Learning a Single Convolutional Super-Resolution Network for Multiple Degradations
- 10. network embedding