之前和社交網絡相關的研究，大多數都非常關注積極正面的用戶鏈接關係；與這些研究不同，我們研究同時含有正面與負面鏈接關係的帶符號社交網絡。具體來講，我們特別關注如何在一個帶符號的社交網絡（該網絡又稱為“目標網絡“）中可信並且有效地去預測鏈接關係的符號為正或是為負，且該網絡中僅有一小部份的鏈接關係符號已知，作為訓練樣本。我們採取遷移學習的機器學習方法，借助於另外一個帶符號社交網絡（該網絡又稱為“源網絡“）中充足的鏈接關係符號信息，從而訓練得到一個有效的鏈接關係分類器；需要注意的是，該 “源網絡同“目標網絡，在鏈接關係樣本和鏈接關係符號的聯合分佈上，並不相同。 / 由於在帶符號社交網絡中沒有事先定義好的屬性向量，我們需要構造一種普適的屬性特徵，從而可以把“源網絡“的拓撲結構信息有效地遷移到“目標網絡“中去。借助於構造好的普適屬性，我們使用了一種類似AdaBoost的遷移學習算法，通過調整訓練樣本的權重，從而可以更好地利用“源網絡“中的樣本信息輔助模型的學習。我們使用兩個真實的大型帶符號社交網絡進行實驗，結果顯示我們的遷移學習算法可以較基準方案，在鏈接關係符號預測的準確度上，提高百分之四十。 / Different from a large body of research on social networks that almost exclusively focused on positive relationships, we study signed social networks with both positive and negative links. Specifically, we focus on how to reliably and effectively predict the signs of links in a signed social network (called a target network), where a very small amount of edge sign information is available as the training data. To train a good classifier, we adopt the transfer learning approach to leverage the abundant edge signs from another signed social network (called a source network) which may have a different joint distribution of the observed instance and the class label. / As there is no predefined feature vector for the edge instances in a signed network, we construct generalizable features that can transfer the topological knowledge from the source network to the target. With the extracted features, we adopt an AdaBoost-like transfer learning algorithm with instance weighting to utilize more useful training instances in the source network for model learning. Experimental results on two real large signed social networks demonstrate that our transfer learning algorithm can improve the prediction accuracy by 40% over baseline schemes. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Ye, Jihang. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 51-56). / Abstracts also in Chinese. / Abstract --- p.ii / Acknowledgement --- p.v / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Related Work --- p.8 / Chapter 3 --- Problem Formulation --- p.13 / Chapter 4 --- Feature Construction --- p.16 / Chapter 4.1 --- Explicit Topological Features --- p.17 / Chapter 4.2 --- Latent Topological Feature --- p.19 / Chapter 4.2.1 --- Optimization Algorithm --- p.22 / Chapter 4.2.2 --- Convergence Analysis --- p.23 / Chapter 5 --- Edge Sign Prediction by Transfer Learning --- p.28 / Chapter 5.1 --- Transfer Learning with Instance Weighting --- p.29 / Chapter 5.2 --- Training Loss Analysis --- p.31 / Chapter 6 --- Experimental Evaluation --- p.35 / Chapter 6.1 --- Data Preparation --- p.35 / Chapter 6.2 --- Evaluation of Transfer Learning with InstanceWeighting --- p.37 / Chapter 6.3 --- Effectiveness of Topological Features --- p.41 / Chapter 7 --- Conclusion --- p.45 / Chapter A --- Proof of Theorem 1 --- p.48 / Bibliography --- p.51
| Identifer | oai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_328643 |
| Date | January 2012 |
| Contributors | Ye, Jihang., Chinese University of Hong Kong Graduate School. Division of Information Engineering. |
| Source Sets | The Chinese University of Hong Kong |
| Language | English, Chinese |
| Detected Language | English |
| Type | Text, bibliography |
| Format | electronic resource, electronic resource, remote, 1 online resource (xi, 56 leaves) : ill. (some col.) |
| Rights | Use of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/) |
Page generated in 0.0025 seconds