【論文筆記】Landmark Indexing for Evaluation of Label-Constrained Reachability Queries
Our contributions 在我們的工作中，我們彌補了這種情況，提出了第一個LCR指數解決方案，規模大圖。我們的方法利用了基於陸landmark記的索引這一概念簡單的思想，這一思想已被證明對於標準(無約束)可達性查詢處理是成功的[1，2，19，33]。簡而言之，我們的方法選擇少量的landmark頂點，並預先計算從landmark回答關於LCR的查詢所需的所有信息。在查詢時，我們基本上執行
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相關文章
- 1. 【論文筆記】Answering Billion-Scale Label-Constrained Reachability Queries within Microsecond
- 2. 3-HOP: A High-Compression Indexing Scheme for Reachability Query
- 3. 論文筆記：An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
- 4. Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval 論文筆記
- 5. 論文筆記：Evaluation of local spatio-temporal features for action recognition
- 6. 【論文筆記】SoK: Security Evaluation of Home-Based IoT Deployments
- 7. 《Who’s Afraid of Adversarial Queries The Impact of Image》文獻閱讀筆記
- 8. 【論文筆記】On How to Perform a Gold Standard Based Evaluation of Ontology Learning
- 9. 《Efficient Batch Processing for Multiple Keyword Queries on Graph Data》——論文筆記
- 10. 論文閱讀：RUBER: An Unsupervised Method for Automatic Evaluation of Open-Domain Dialog Systems