臺灣博碩士論文加值系統

由於網際網路包含豐富的資訊，可以將這些資訊建構成一個異質資訊網路，但因為其中可能包含許多不需要的信息，要如何從中挖掘出真正有用的資訊，是值得我們探討的問題，期望可以設計一套方法，從網路中取得重要的資訊，以了解節點之間的相關性。我們的任務是預測用戶的社交連結(UU-LP)，以及用戶與項目之間的連結(UI-LP)，倘若能夠精確預測兩者之間的關係，表示該方法能夠正確捕捉到節點之間的相關性，進而可用於商品推薦等等。我們提出了兩種強化異質資訊網路中的特徵表示學習方法：metamotif2vec與diversewalk2vec，metamotif2vec設計一個結構性的隨機遊走，可以同時考量較多不同類型節點之間的關係，而diversewalk2vec則是設計多樣性的隨機遊走，不用事先定義隨機遊走的形式，透過讓路徑通過多種類型的節點，自動捕捉其中的相關性，且可設定一參數讓隨機遊走可以傾向在同質網路或是異質網路中進行。我們在Twitter打卡紀錄及Douban Book兩筆資料進行實驗，相比於目前最先進的異質網路表示學習方法metapath2vec，我們提出的diversewalk2vec與metamotif2vec在UU-LP及UI-LP的任務中，平均可分別獲得7.1%與5.2%的精確率提升。而網路雖帶來了生活便利性，但也產生了隱私風險的問題，因此我們設計一套擾動資料的防禦機制，同時也進行連結預測的實驗，評估防禦機制的有效性，結果顯示其確實能使預測精確率下降，因此能夠降低用戶個人隱私外洩的可能性。

Since the heterogeneous information networks contain rich information, it is worth discussing how to extract useful information from the networks. We hope that we can design a method to preserve both structural of heterogeneous network and correlation between nodes. Our task is to predict the users’ social relationships (UU-LP) and the links between users and items (UI-LP). If we can predict the relationships between users or user and item precisely, it means that the method can capture the correlation between the nodes. We propose two methods, metamotif2vec and diversewalk2vec, to learn a low-dimentional feature representation for each node in heterogeneous information networks. The metamotif2vec model formalizes a structural random walk, which can consider the relationships between much more different types of nodes at the same time. On the other hand, the diversewalk2vec model designs a diversified random walk to capture the correlation automatically without defining the form of random walk in advance. Experiments conducted on large-scale Twitter check-ins dataset and Douban book dataset exhibit that metamotif2vec and diversewalk2vec can average achieve 7.1% and 5.2% improvement over the state-of-the-art heterogeneous network representation learning method metapath2vec in both tasks of UU-LP and UI-LP, respectively. While the Internet makes human life more convenient, it also raises privacy risks. Therefore, we propose some defense mechanisms for disturbing data and also conduct experiments for link prediction to evaluate their effectiveness. The results show that the defense mechanisms can reduce the possibility of leakage of users' personal privacy.

摘要 i
英文延伸摘要 ii
誌謝 vii
目錄 viii
表目錄 x
圖目錄 xi
第1章. 緒論 p1
1.1. 研究背景 p1
1.2. 動機 p2
1.3. 研究問題 p2
1.4. 研究挑戰 p3
1.5. 方法概述 p4
1.6. 潛在應用 p5
1.7. 論文貢獻 p5
第2章. 相關研究 p7
2.1. 特徵表示學習 p7
2.2. 異質網路探勘任務 p9
2.3. 隱私保護 p10
第3章. 問題定義 p12
3.1. 網路定義 p12
3.2. 符號定義 p13
3.3. 問題定義 p13
3.3.1. 攻擊方任務 p14
3.3.2. 防禦方任務 p16
第4章. 攻擊-研究方法 p17
4.1. 研究架構與方法流程 p17
4.2. 特徵工程 p18
4.2.1. 基本特徵 p18
4.2.2. 特徵萃取 p19
4.2.3. 特徵學習 p22
4.3. 連結預測 p32
第5章. 防禦-研究方法 p34
5.1. 研究架構與方法流程 p34
5.2. 資料擾動 p34
第6章. 實驗評估 p41
6.1. 實驗設置 p41
6.1.1. 資料集 p41
6.1.2. 比較方法 p43
6.1.3. 參數設置 p44
6.1.4. 評估指標 p46
6.2. 攻擊方表現 p46
6.2.1. 社交連結預測 p47
6.2.2. 用戶-項目連結預測 p53
6.3. 防禦方表現 p57
6.3.1. 社交連結預測 p57
6.3.2. 用戶-地點連結預測 p60
6.4. 參數敏感度分析 p63
第7章. 討論 p68
7.1. 方法之穩健性比較 p68
7.2. 防禦之應用 p70
第8章. 結論 p72
參考文獻 p74

[1] Michael Backes, Mathias Humbert, Jun Pang, and Yang Zhang. Walk2friends: Inferring social links from mobility profiles. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, CCS ’17, pages 1943–1957, 2017.
[2] Lars Backstrom and Jure Leskovec. Supervised random walks: Predicting and recommending links in social networks. In Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, WSDM ’11, pages 635–644, 2011.
[3] Smriti Bhagat, Graham Cormode, Balachander Krishnamurthy, and Divesh Srivastava. Class-based graph anonymization for social network data. Proc. VLDB Endow.,2(1):766–777, August 2009.
[4] Alina Campan and Traian Marius Truta. Privacy, security, and trust in kdd. chapter Data and Structural k-Anonymity in Social Networks, pages 33–54. 2009.
[5] Chen Cheng, Haiqin Yang, Irwin King, and Michael R. Lyu. Fused matrix factorization with geographical and social influence in locationbased social networks. In Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, AAAI’12, pages 17–23, 2012.
[6] Sean Chester, Bruce Kapron, Ganesh Ramesh, Gautam Srivastava, Alex Thomo, and Sistla Venkatesh. k-anonymization of social networks by vertex addition. pages 107–116, 01 2011.
[7] Yuxiao Dong, Nitesh V. Chawla, and Ananthram Swami. Metapath2vec: Scalable representation learning for heterogeneous networks. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’17, pages 135–144, 2017.
[8] Roy Ford, T.M. Truta, and Alina Campan. P-sensitive k-anonymity for social networks. pages 403–409, 01 2009.
[9] Huiji Gao, Jiliang Tang, and Huan Liu. Exploring social-historical ties on location-based social networks. ICWSM 2012 - Proceedings of the 6th International AAAI Conference on Weblogs and Social Media, 11 2012.
[10] Huiji Gao, Jiliang Tang, and Huan Liu. gscorr: Modeling geo-social correlations for new check-ins on location-based social networks. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management, CIKM ’12, pages 1582–1586, 2012.
[11] Aditya Grover and Jure Leskovec. Node2vec: Scalable feature learning for networks. In Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16, pages 855–864, 2016.
[12] David Liben-Nowell and Jon Kleinberg. The link prediction problem for social networks. In Proceedings of the Twelfth International Conference on Information and
Knowledge Management, CIKM ’03, pages 556–559, 2003.
[13] Moshe Lichman and Padhraic Smyth. Modeling human location data with mixtures of kernel densities. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’14, pages 35–44, 2014.
[14] Hao Ma, Dengyong Zhou, Chao Liu, Michael R. Lyu, and Irwin King. Recommender systems with social regularization. In Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, WSDM ’11, pages 287–296, 2011.
[15] Kamal Macwan and Sankita Patel. k-degree anonymity model for social network data publishing. Advances in Electrical and Computer Engineering, 17:125–132, 01 2017.
[16] Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient estimation of word representations in vector space. CoRR, 2013.
[17] Bryan Perozzi, Rami AlRfou, and Steven Skiena. Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’14, pages 701–710, 2014.
[18] Adam Sadilek, Henry Kautz, and Jeffrey P. Bigham. Finding your friends and following them to where you are. In Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, WSDM ’12, pages 723–732, 2012.
[19] Madhuri Siddula, Lijie Li, and Demin Li. An empirical study on the privacy preservation of online social networks. IEEE Access, PP:1–1, 04 2018.
[20] Maria E. Skarkala, Manolis Maragoudakis, Stefanos Gritzalis, Lilian Mitrou, Hannu Toivonen, and Pirjo Moen. Privacy preservation by k-anonymization
of weighted social networks. In Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012), ASONAM ’12, pages 423–428, 2012.
[21] Yizhou Sun, Jiawei Han, Xifeng Yan, Philip S. Yu, and Tianyi Wu. Pathsim: Meta path-based top-k similarity search in heterogeneous information networks. In In VLDB ’11, 2011.
[22] Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. Line: Large-scale information network embedding. In Proceedings of the 24th International Conference on World Wide Web, WWW ’15, pages 1067–1077, 2015.
[23] Dashun Wang, Dino Pedreschi, Chaoming Song, Fosca Giannotti, and Albert-Laszlo Barabasi. Human mobility, social ties, and link prediction. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’11, pages 1100–1108, 2011.
[24] Hao Wang, Manolis Terrovitis, and Nikos Mamoulis. Location recommendation in location-based social networks using user check-in data. In Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL’13, pages 374–383, 2013.
[25] Yang Yang, Nitesh Chawla, Yizhou Sun, and Jiawei Hani. Predicting links in multi-relational and heterogeneous networks. In Proceedings of the 2012 IEEE 12th International Conference on Data Mining, ICDM ’12, pages 755–764, 2012.
[26] Mao Ye, Peifeng Yin, and Wang-Chien Lee. Location recommendation for location-based social networks. In Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS ’10, pages 458–461, 2010.
[27] Mao Ye, Peifeng Yin, Wang-Chien Lee, and Dik-Lun Lee. Exploiting geographical influence for collaborative point-of-interest recommendation. In Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’11, pages 325–334, 2011.
[28] Jia-Dong Zhang and Chi-Yin Chow. igslr: Personalized geo-social location recommendation: A kernel density estimation approach. In Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL’13, pages 334–343, 2013.