論文User identification across online social networks in practice: Pitfalls and solutions研究總結
文章通過社會網絡的官方應用程序編程接口（API）的公共屬性獲得數據集，提出了構建超越常規隨機選擇的負面實例的不同方法，以研究每種方法在訓練分類器中的有效性。爲了評估不同分類器的健壯性，建立了兩個具有不同層次的區別的測試集。通過匹配從Google+、Facebook和Twitter收集的個人資料，在真實情況下衡量這種方法的有效性。 Keywords：Classification; online so
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相關文章
- 1. 【論文總結】Aligning users across social networks using network embedding
- 2. SynchroTrap：Uncovering Large Groups of Active Malicious Accounts in Online Social Networks（論文筆記）
- 3. SybilWalk：Random Walk based Fake Account Detection in Online Social Networks論文筆記
- 4. Friendship and Mobility: User Movement In Location-Based Social Networks 閱讀報告
- 5. 論文筆記：Predict Anchor Links across Social Networks via an Embedding Approach
- 6. 論文翻譯：On Finding Socially Tenuous Groups for Online Social Networks
- 7. 500 Data Structures and Algorithms practice problems and their solutions
- 8. Device-to-Device Communication in 5G Cellular Networks: Challenges, Solutions, and Future Direction
- 9. 論文筆記----DeepWalk: Online Learning of Social Representations
- 10. 論文筆記：DeepWalk: Online Learning of Social Representations